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) 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g5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg+FB1ubjlnRNvl3o6IEU2w7fdNPhm/hh+FLysUu6++DLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK/m4szFFR7ch0toUgBTdWHr7EpaWru6+6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d/jLST9v5YflasP6v/CO7+GNAPC2BMZWmsOjp2NNbfHwMCJD+LPVL+D/OYlWEEI/9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2+bBDWBud1Va4pcCn8CPqxlh/fgtG8IPaPH8C5wk6/nZDv69jurV5QhtwE0x2iqOsj9Mx8B9/0EaUdiPfOYYDCi/q9jhWRuupMDEU0+CtX0sDFxv07T/K5niBPqN9+tQjgEc31NGCXFeMcCEuQBIc/BK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF/R0/u5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y+RBgogrXPZ8E1yIHoHIFUM+AbJhE7lbMtt8ApL+xmZW7PwbjAO0fAVoXQOuiSP/ksIVdFZ0aulsamKUzwPZ/NYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7+pg+jUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd/J5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7+RHJHP+2hqfugo/MvI2H/mqr4b9tFnKSRY1Y5Ek80Nm/WIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw+nSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv+rWa/Wih0vlbX6Zb75T5C0qNKVFvH1QL/vazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f+XdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS/xTo+P5wGFak62ap1PVFFN4v/y+xuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f/6dhK6gQokFKhWX3urrjk/rnI0pgfpGMeuQIUaEM7+GF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x/GdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY+9yGFQtEo0GQPNv6vS2drj4+1jHbv3aJSMUWP+QTZrmbNTjU8wyG/iXNNpskybLcJ3CiTF5Ir+JYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d+/XeNRTZ9/1OPfG7+2hwzd5W3D+hmyjsRcUg/+Cavb++Vh2ls3L7zT/etOnHNxeerv313vzLVqPai4nJv+K1FC6040/4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk/U+D4gTXW+j0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7/R561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l+WKHmLTJwRv9E8GWJ6dYvf/FmEyEGr+gyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b+tMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1+Q2BIRvf6J6Kgatnrbiem+CFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ+CuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih/xELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD+uOe+kWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby/+SSbPY8/NH6vpl/Esq3Ae4ZU1HC44KFiI9o7CEgab/RqHbj7s5KAg06s39ZP/zxI/mVuF/TbTSy+3Fb8If9/cv7+wt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi/C8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0+Xxx/5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB/qM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J/UKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc/THK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd++U/3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg+e4As38MfGquNt7oUws6Ysarj1/efE+yst86YUVNvDdts3Pv5c8m/aP0C+f8/Qb+IMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT/v27wm09jRYVc/e++iyx2tyzJb/n3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy/t27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj/S5ki1jL/a0GC6+2L6Um+aoddlNFuj+bJ8mH/iaLh8I0/U51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz+1UmhTJyJGxZzw+wsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ/9pPA4RKTASWahmh+8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ/tHnODtENw/2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8+/Elg5j4Gxur3J8o2PJ4rg+2d08T/fwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r/aMMj8onCP/DuDZOuN+GPPr/+p7bx+7JlbYdppcNhzKU/1Px5aiaGDn/s1iGMaBcleKUo/v9rcxkZj7DBEKOfrayytXNLYiUdBY+pleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF/Qa9Pdf/QqOSqCiE/EE1/XIVKTc2tzWbHnimrEd+Vyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu/kzIdx+BxC0ay9zRSvoS0F2lIxI+X7klU63I40gLQ3w5ep5na+SFnba3z5D64zv+QtM4n4ffG3tq4aNHGRfxgrXPMim+5487abL7xhdseIRn1KDl+7aINixdv0OD+JSPwKf5+xoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz+9a/v2PH1Hfz9236w+ZrPXvWfAxlj4NLLHpq3c/PQ3uvmvbrjG7fe+o2y/cLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF/tkkOGPW2ibWaBTkeZ/dvPWazXfOnnvL6jkRXpi85sFzZt+55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG/yVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6/OU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR/rg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x+MJiOA3YwhDRQrWU0u/0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0+a0uoiFH/xcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL+j3D/uwhBRdyAyozeZwvQzs79soi+BKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo/ybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM+3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho/VDkzQfLE+IrYoJXkD19pdP7OwG/voIUtagiWiZ4PAFTHHlTVhRZ7dYmPar+NJ+8JhmR6DFK5DV1foHoLNO/pHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj+i5++WZfKeViJfW5HnUakVL4UCNVkA4+ETfIqx4B5xSaP2L1yn0zn2ltPn4+OqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL/NNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE+cPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP+Kps8J9/3mGQIteY/LGPC+L7872SPR2br5fy8MtKBMHedGuM28/MZmPJMrGgi3Gb1S+Si1/L/zrZwO9XH1ce/z7ZQ1WSoY/+pMb5FT4ua0Wm+Jf/298nFmChEQ+Ti71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa/1IFO1/jdWr/8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc+KJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3/tM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB/yox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt/qMDnvLpPNTXfNa60z5/yjXQOMq+lNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd+RiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb/pBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap/30lnVESgNnvjbUoT6w9N+Xoio0qcYOIM+heg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X+PE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa+dAdAbMYX6HVF8A+7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf/Nf+Ny65BhYxxxV+77XJ2wfQ389/IQPgajXbwMsuAz/0IaQcXJavKbRqR2IqyZruXjVC2+hdee/5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab/wXl2CNe7s/qCtTvWgG5kpBmCBlSzDS/r8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8/NCfiOUVirYcBbIeA2+iF68rQIo3B/S628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935+k1efkV51mzzrM0LL3/20avnwMeKuWyOUZg2TasSqZ+KcZQiOn1Iu2Vh497ALUVZiCKt/gh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1/uzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb+okJ5JRDyf54Axur1D+WS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt+t/qW2+93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz+tJo+wijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF+kuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj+Z4i+42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA/hRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz+x+Fkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3/RTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY+uSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN+fiK86LeXj+Sc5lPKy+k/vCUI/DaLFYCWHr6nbXuILTIsb5imNKY/rCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T/yOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH+L0eCPxmsa5ZpgRJSDZ11yDv+jmbd86vxZfc1WcZJ3UkMq1BOOOVtvu/+pB+en186d3GTwWAw2jheaJs09/+LNfZft37DALyrNj1wABMuUKbODyTVnT/KYbJ3Tpq8IrNh92dkxOj5P/YpZx4/ycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB+cV99/eTF6294dA8+TK6v74MHVpYNRt/I30e8QGTOOdfGWzzxcy+87a7bLjw37rHw1nPzp0KyyRSeZO+QQhInt3dYgvycjrPOv+T8s1rptaP84VeywdWX2T4ysr0/7TLIs6+x9zib56ye1dM9e/XsZmePY3NDs9zlnNVt4+WgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS+XPplV2ts4bvCwZu+KzgITtxepaPRzWdpv74muvv6RO0SorX6cu/dqKn/XWnrtp/Zragz13DUCl5myiFW2Ycvb0PtsXnU+tx8pvLFbUspLX68mdegwmOif/NPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3/jdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx+6arPqbkcNAHoFPzKUUQ+qL0k97pjbZv1I/egC9zTFbrrlFpNdmea+gIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV/ScS+zjlASyUTVv/AJ46gkJI4bHX4lTnloctxPZE1ckS3+jG2fKIjkQFyzuo8jvYQG1OrGvJPSTu/nSp9PHNTl4z5hK/8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt/wkSx/JCmP5/cBKQfl/5gd+rOS/+p91/+YCg5CXK2W4M9fu+/6xxX+vnelVuldIDCG0VQTpU9Dw4pRfei+6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15+5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl/5bqzYqT9hMmptEXDgTqP3Wiye+sD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC/v8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4+Ti7W9D/OZrLChdwxmPNiBRqVjnpK/aGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3+OfU5/ieLS/NuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD+N2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad/3t2KTtPobnX6D8C8pd0MDP+Kx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE+JGTS3JoEhTrz8/CB+5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk+htwN5A0V3RCPoD/yXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps/t+0iCT3AHVtZC7JDCXfR7OSb/Xja5H3zQbZL1B+ULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset+nQWdS/ZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5+LhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW/3Q5Nn8BZEuzqEI7HK3n0+zFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj/mQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2/BWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY/yCZu/Amd5Or9uS3DYaeqVOhH7gZN/8I/wi1fEuLXvyNivibjuKvN+1Nc01HF/3h+ef/sOhox8MPd5SFucPjorQwXT+ytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP/79nbvCXFOY+PUjrT7/otsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9+pbs6E/Vu7Nk642/PYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu/19/5QW9Rv1U1ridT22i+53pzumbs+XFFXYC++CGsTj5JUT/GCgRt3n78i2n71FHG4/u6X++9+raya7os3ZbDmgWfXun44e+u2NZKuGZ0HiF8M4TlMPR+EU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l+uXrbjPUyep+8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq+NzK/OKSbsfl79o9G20R+brBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery/7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1+SdazRrfddkBU98t1htvWrbjqSqjaCguxrffM/5zDCpBALUycmajhd+R6ww4SWafuZ5eU+tPid4lgd3gt+b/Y9rQoZNmiXYPXyRHbRs8zX/f4WIFjWZJtUdSD55AP3xtXH+ZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy/8F0GoqYPv75Yh9j3x4DuJ+uEzHRpAq2lMqb+qfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT+LUgx0Pg/p87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv/u9ZUWUx+PYFueUKwaNvbtu+Xps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L/BehXuHqfaBwBEU7hfVLcXvS4VQv+T/vaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq+k4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S/tkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP+EaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP/dsZ8+fP75D/9Uu5Gs3FY/2SxPld0DHOciXI9gqjcEidXjE+3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8+LTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7++wW4gRDRbaxw2nrOGm+xOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot/FpHvIUjJovFEoYvHYV9C5Y/xN9OfcalvII37UEhTbTg/AQIaPb4Vz6j5u8/aViycMod/fkDcpu8QZbZoeBi/vbzP3XPsZvOubMtaPHkD9jt6+U2O7vqU/9C9SMvgrXpQNG/E0oJxun+CiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp/VAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y+pGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr+P2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr+P30f3OkPQ72aO0xYo3/EsmO3QO5qEF8S0qQH0UsKXv0brnl9+8M7jF174+DsfvPOl1au/RL5/9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x+4ofw+3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ+pYl+Sf8YcurfmXP5F/kj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0/OM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw+1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g+Rn533qkocdvLs2HmhU75br/MmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3/3pYNi3bS+Rd+GjOfVpAPNd6y64Gsz1GaZleWIPoYL/v9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk/Iojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB/36kP+K38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL+x03nhh+oNo5e77psxg9Q5LzebIKD+fsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O/kdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P/F7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF/BRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm+RdmIJQST/ZfVdudUvh9S/qqNvqT98g9SQ3lHibZY0mRVHooyDN/FHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi+d0s4+ztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr+1VZFcEZ/PdJkn1hOs8SXS/NFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq+rbyg1Z+O8VsNC1UmcvORPRfxtPrfRwL2p/oA1eZp6Z/aGffoewaXcA/xBlKlQLfhQL/oPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp/bXh5e5YXW2Ngfvza0ZF6UgFL/E0fTq4LBlvTE2qb/KuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8/VWi33c3bBZnGY05+dm+3qc7fNmj4YGKLj2nfqFP+g7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j+SYEWHlZ0K/Hgsh/Ztsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D+YVyKd0jw3iumwi/BC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc/J5uGm6CViW5mulYMk+HqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br+fcLyoAyu8D9B7bgmzUqfFobF5nKnK4+t8MPJkI/xHUNWk117jugWF+xazTAALQn6+UE9lhoI5ApGA/iuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL/3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n+jspIsHFjJrTOdzSMuOa9DbDcj/nH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U/8JbJZPJJLBLolH1La/RbF9AbC8JJjv/mMnssKjLRBJyqj9QXxNko0Ux/X79epfiXkm6fmKwF/en1HLc6LxloXWKvGa5rVCVL83VuiPcDEX/K5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS/kTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv/36k2sbqCeCFNa+URpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR+D79H56NOz0EVWCTy2/fffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ+T/El3uZqL+FyzSZ8XxpTiI/G0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ+euOeG2SORmKZr/qKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya+vlKqv6aXK1qIj3imL+L6hL+yvUlFfE0VKZ7E8gBY3M/8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry/LgZIlCeP11Z4zs/AwvVwtGFEut5S1JY4lfyT0N/evOLo+rUEgjcqc9IkGpQbv3iW7Co5b+KgjvpzYdH85PLcc4X21ouwEGl/S4qnUAvoSlXUUhR1eKr2VWFTB+GMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH/qPepXQ0Db77CJOAImohB+RPWr31ev5g/kE+zTa4lbvZo8xdWPffQu9yJTPCNB66s+zXoJt/0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0+3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80+UxfUHPlBZIRVNQ+v0xRm8REKPoLmNr0+Uo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV/+mfr0C0r/NkeXbbpPlOTBBwT0mVz1zx9S/wJecBF9Wgv3p032iP2v4VSgfgW2G+HUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt/QRasn2kri+H6L80MJRsmVYJrAKyDItpJUy3/15WYIJqcJ9Q5N/LFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb+IP8gHed+6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha/fJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG/XbOFz+Tcq2MLrcmV28Nmi/OHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw+luPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs+31Wm5qpH+iJWSzqdPr2O7zc2TmtnrzCig6bBd/vgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I/MAp9q9S3Q/clADNm1wEeO+xbwg5OIYHZLY3ehG5lJk2xhco+6JWybpEVz2wrR6hZyD0QXZbeDVB+onmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P+1fAll+SyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y+cf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY/f+ucuMtCuP/e4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn/+7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7/6B23+f+sp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz/Q0hI0m3NKDRfkgsrnamo+p0CAq1FyvC3a3Nak/s5VX282x9Ufy3E39VAx6o7LpCvO2wK+ch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5+jP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00/Wavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR+gufwA8GegxUEo4A+gd0BASHiH6jYMLIsUdQJTs/C641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW+AMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA/xUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA/E+DXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX/k/7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh/TK18StOPhwG9Xo5mqXAxvw/79YQwwDR+nAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw+oTFqxtz8dxwtogBnF9MjIl/in+K8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O+GC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6/OC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx+v1UKPsfvzQU0Kc83i2VdILmd2/yX55zT9luN2+Cu4nKfwPcK/CvDVU+pHh8+LaldIf1fA5h3ndT6Fln9/W/9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW/uib/ud+VK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M+VG0KsUdgdvpgP/6NqqP0/gHZFUfGEijZLHpiIgvV5/Bltrj8Qd7XQd5p4P+7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z/vvacG/rjpsvnpzs0B1Id6fmX8119l88XnOxe/uGrzzHcdu7UtY3+2vmXN5zUyj3ZcPl8p1sZSs6/nGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q+lwg1RdRSpFDNzosb44YJXnAfoEOpZW//6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21/BWW5DnPQbn2X1WK43/aM2n18DfSoybbNHijFpamzXI31eRibGUOxSu/lT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO/VjC/EinToFyoNDz2p9XD6g78WgQdPG7Z3R0T/Z5dTM9lsL8Ktek7szl2L+gQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T/4VeamYPlLtffdQsk+2sUTY/zDXl/05W53/Bz9UK3p7LjapZ2ZxOm+UlZXrL3HHGqO8+wVroDaCTTnTxitMxmiAAYQzVJQH+nj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj/9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz+3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62/qN/tjQxnSnhnKPw+LNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh/LvQZnt0evgIhTFV0kE/PYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8/Cj1Y9NY6cnGVW0CjTLaz7E3epvaT/LtTV72Rs+0WVVmd0dz/MGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf/z+fdka+eKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ+bewnf18ap4Ib+mSMrtDU5zqlD8QANa5MBGh/OwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq+7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2+YuMbmE9dwGYB4KFTsvnDE6Ah47r/fE3AYI+oXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU/A/Jsw+vWDa/7emewLaoeibaF1B9qUNnuqWK3+UfXYVL1v/omD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk+gk8pL0mcCDOn08MDAQlf+/SlTZ1z12fnqntOhbOTL9/ZdevbAPN+yby1f/uUtC/ixm8ZBo59LTXEW060hGrTDplNprWd58fwB/b/E27BdS/s7U+rGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt+Kh6HN4ti6lxIhT/wahnZtWwzlY9QHQ2c79C+dxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah/PFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn+2MnYv36dbMDBTonl/T/ma5IKAyEGz+4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK/yp/gLEBVat5WnPDdKA574ubuFJ/IUfZ/Y2Nt6mN+ZNNTSTaeI56gKwkXerTe9DDHUw8/H35FY3nNN7GGuBKWhrV9ep+0k1WjNWVaHkW1yA+QHWNu8rtBw2a5YXuE40rs7/GA+j09V3hA98yRnFPOGr8ltGlsFdD/7tRce3LH6Trcneuiy7K7J3khKu+3qUaXPWaX7T6/Kfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM+0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz+XmcOs1H31U6cC6RTVLfCg7RNBF1UF2/wBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH+WC0E1qOGtP66s1MYv+VLu8/XFXvV/ZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86/1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp/lYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1+x1SXwLpFi53b+aHRYo/IrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f+nAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g/VRdpkkyVpD7jnmax88QwDvg/66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb+YFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb/d5gqmNaqJZSO1l6PVRy0nZIj/45EetjLguh1rLqR+SK0hO6NrsqcNX8zoUdjQYDJ7tb4os6+i+Y0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY/gZmE9iuP4V5v7CSR/RThaHLh+UeBkXwU6BC7lGOevK65udTv+tS/PfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso/c/1K9aLE12fMWLV1y1D9ua8lyJdWXr/bG+noCFutf/mLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq+PEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj+nzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3++fcnT49Qe+kBs+wdsMZgPXMp3U5S958snPP/EE7bvkOPCuTUDTUQ/UzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk/seDVWsvDWXXXsyn/4wfpXc2V3/Qxli3jl/5hj/83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3/+2TJrzLlkFz+ip3qCHKf6eqW02QJLjBYuuj4sobhCWqa/YHGEHpcnumuWSOhxeaL7sOakNR6vvmo+YcfFA8UFXEPZf9UjyudIOyNwx/i90DdsujS/FX2UAwvWSVK4NxaMhAGw3oowp/uc8CTi7D2rBgZWwb/60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E+o2t1nePTn8TUmxz/A3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD+qJopELrl4S6cJNRtEAsLP/OdvnJq0Wo0GolY2Et9VFB2Kf+4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam+bWmUJZfNn5ozKV5Pmmq8PF/jVY+2Tlk2M2RzSXKjmbQ4RZcQavEYrN/9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd+SyX/Cyf0Vxjd2R9NmsunnXYa8xGHzn+sSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV+D5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H/lpZBicglCPN7WOfJkcHqc3ElWqvvz/1E6bIQrG+tz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw+WSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM+wEW0El9dM/ueVRWIsXwe43R7SgbVZqrnqoJ1X/kuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM+foaCcwgaoqZUtmodV3p+X7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z+O4fYIbvZVt4EUcJEKOhHeincWqONw+q6w5Go+WGOSR7LhKV+KBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx+SyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq/qlFo1v+PTmTi3E/glC9W11b34A22zmLzvb231Q0L2Bgg60OTW4YdstO+YOJnO38TtpH7zy9ymokWyA79qlVSn38HtpFlImFnhu3b4boNWXklOXV0Iwo7lQ1hrZyPFcwtjwFP7iEKSHSSJw509kh8kj6pr+H1jR7km9vcvqN9657vffefkv+fKxge1X+7RdjYUPIESN7gTvRkB/RMYtEkaVkdHApmdBPpnKmz0n1xSWFOyVIuLrinZwpoCRe6kyiVZoHX088F+UX4+WKS4iBTP0IWxGtZgOdMaV4KTayqHQF/VihBwTbgDXTCmKoOBJeNhwJMzEVjtjIFLuU38fPR7hqNG1JS7g/qRCuy3vmQ3W9Vu8qbVbP+SzazGRJH83MzP90Ck2m31mMjP8TiLn5uwD2Ugr2PFvPQjB5BnSJvQxGQZZEB+LopqzGzDbMmbkAPkZVJjeO5FzOSBKCgJze2ZS4Gemc9twrwY6u9H61iUQTcRvtdT9RW3tRxAWwFs2tcuJRnI6xjmBdWjbgFNRHMHiF1uHYBfUR/ut5Ug2jXAaT96+9RH/FToRwIzGbKmVJ1AZQnoabSB1yyIg7ByAridHApPMjyw0OiV6RjSbCuzwLAvFizBliWJua1tsuAgvNPbmljYbpt8lkWam7b3XZiOiKJskMOtmfScnsbPW208knwjuXrXK4Q1iKIgNyYXXDVT9C2Ye/78GQ5BEEXfFdde2RwauOysdJNL5AzCy84ard/nGAVN8alecnFdgu5Gbd5DJTL+hHZK0vApVy3OfU8XTSJg1TlssivsPYUlIqvn66PzrVTymCc4wgF6SDNR0pDf+9Gp+VnsUH5WtpHYsuhOaey8zdwLN47V8MTbm78g687+P3cx6tcAeNpjYGRgYGBk8s0/zBIfz2/zlUGeZQNQhOFCWfF0GP0/8P8c1jusIkAuBwMTSBQAYwQM6HjaY2BkYGAV+d8KJgP/XWG9wwAUQQGLAYqPBl942n1TvUoDQRCe1VM8kWARjNrZGIurBAsRBIuA2vkAFsJiKTYW4guIjT5ARMgTxCLoA1hcb5OgDyGHrY7f7M65e8fpLF++2W/nZ2eTmGfaIJi5I0qGDlZZcD51QzTTJirZPAI9JIwVA+wT8L5nOdMaV0AuMJ+icRHq8of6LSD18fzq8ds7xjpwBnQiSI9V5QVl6NwPvgM15NXn/AtWZyj3W0HjEXitOc/dIdbetPdFTZ+P6t+X7xU0/k6GJtOe1/B3arN0/pmz1J4UZc+D6ExwjD7vioeGd5HvhvU+R+DZcGZ6YBPNfAi0G97iBPwFXqph2cW8+D7kjMfwtinHb6kLb6Wygk3cZytSEoptGrlScdHtLPeri1JKueACMZfU1ViJG1Sq5E43dIt7SZZFl1zuRhb/GOs44xFVDbrJzB5tYs35OmaXTrEmkv0DajnMWQB42mNgYNCCwk0MLxheMPrhgUuY2JiUmOqY2pjWMD1hdmPOY+5hPsLCwWLEksSyiOUOawzrLrYiti/sCuxJ7Kc45DiSOPZxmnG2cG7jvMelweXDNYXrEbcBdxf3KR4OngheLd443g18fHwZfFv4NfiX8T8TEBIIEZggsEpQS7BMcJsQl5CFUI3QAWEp4RLhCyJaIldEbURXiJ4RYxEzE0sQ2yD2TzxIfJkEk4SeRJbENIkNEg8k/klqSGZITpE8InlL8p2UmVSG1A6pb9Jx0ltkjGSmyDySlZF1kc2RnSK7R/aZnJ5cmdwB+ST5SwpuCvsUjRTLFHcoOShNU9qhzKespGyhXKV8SPmBCpOKgUqcyjSVR6omqgmqe9RE1OrUnqkHqO9R/6FholGgsUZzgeYZLTUtL60WbS7tKh0OnQydXTpvdGV0O3S/6Gnopekt0ruhz6fvpl+nv0n/h4GdQYvBJUMhwwTDdYYvjFSM4oxmGd0zVjK2M84w3mYiYZJgssLkkqmO6TzTF2Z2ZjVmd8ylzP3MJ5lfsRCwcLJoszhhyWXpZdlhecZKxirHapbVPesF1ndsJGwCbBbZ/LA1sn1jZ2XXY3fFXsM+z36V/S8HD4cGh2OOTI51ThJOK5zeOUs4OzmXOS9wPuUi4JLgss7lm2uU6zY3NrcSty1u39zN3Mvct7l/8xDzMPLw88jyaPM44ynkaeEZ59niucqLyUvPKwgAn3OqOQAAAQAAARcApwARAAAAAAACAAAAAQABAAAAQAAuAAAAAHjarZK9TgJBEMf/d6CRaAyRhMLqCgsbL4ciglTGRPEjSiSKlnLycXJ86CEniU/hM9jYWPgIFkYfwd6nsDD+d1mBIIUx3mZnfzs3MzszuwDCeIYG8UUwQxmAFgxxPeeuyxrmcaNYxzTuFAewi0fFQSTxqXgM11pC8TgS2oPiCUS1d8Uh8ofiSczpYcVT5LjiCPlY8Qui+ncOr7D02y6/BTCrP/m+b5bdTrPi2I26Z9qNGtbRQBMdXMJBGRW0YOCecxEWYoiTCvxrYBunqHPdoX2bLOyrMKlZg8thDETw5K7Itci1TXlGy0124QRZZLDFU/exhxztMozlosTpMH6ZPge0L+OKGnFKjJ4WRwppHPL0PP3SI2P9jLQwFOu3GRhDfkeyDo//G7IHgzllZQxLdquvrdCyBVvat3seJlYo06gxapUxhU2JWnFygR03sSxnEkvcpf5Y5eibGq315TDp7fKWm8zbUVl71Aqq/ZtNnlkWmLnQtno9ycvXYbA6W2pF3aKfCayyC0Ja7Fr/PW70/HO4YM0OKxFvzf0C1MyPjwAAeNpt1VWUU2cYRuHsgxenQt1d8/3JOUnqAyR1d/cCLQVKO22pu7tQd3d3d3d3d3cXmGzumrWy3pWLs/NdPDMpZaWu1783l1Lpf14MnfzO6FbqVupfGkD30iR60JNe9KYP09CXfvRnAAMZxG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<h1 class="title toc-ignore">r_script2.R</h1>
<h4 class="author">ecrawford</h4>
<h4 class="date">2021-08-30</h4>

</div>


<pre class="r"><code>library(foreign)
library(dplyr)</code></pre>
<pre><code>## 
## Attaching package: 'dplyr'</code></pre>
<pre><code>## The following objects are masked from 'package:stats':
## 
##     filter, lag</code></pre>
<pre><code>## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union</code></pre>
<pre class="r"><code>library(psych)
library(broom)
library(jtools)
library(margins)
library(ggplot2)</code></pre>
<pre><code>## 
## Attaching package: 'ggplot2'</code></pre>
<pre><code>## The following objects are masked from 'package:psych':
## 
##     %+%, alpha</code></pre>
<pre class="r"><code>library(&quot;readxl&quot;)
library(sm)</code></pre>
<pre><code>## Package 'sm', version 2.2-5.6: type help(sm) for summary information</code></pre>
<pre class="r"><code>library(pastecs)</code></pre>
<pre><code>## 
## Attaching package: 'pastecs'</code></pre>
<pre><code>## The following objects are masked from 'package:dplyr':
## 
##     first, last</code></pre>
<pre class="r"><code>library(&quot;ggpubr&quot;)
library(&quot;gplots&quot;)</code></pre>
<pre><code>## 
## Attaching package: 'gplots'</code></pre>
<pre><code>## The following object is masked from 'package:stats':
## 
##     lowess</code></pre>
<pre class="r"><code>library(rstatix)</code></pre>
<pre><code>## 
## Attaching package: 'rstatix'</code></pre>
<pre><code>## The following object is masked from 'package:stats':
## 
##     filter</code></pre>
<pre class="r"><code>library(stargazer)</code></pre>
<pre><code>## 
## Please cite as:</code></pre>
<pre><code>##  Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.</code></pre>
<pre><code>##  R package version 5.2.2. https://CRAN.R-project.org/package=stargazer</code></pre>
<pre class="r"><code>library(devtools)</code></pre>
<pre><code>## Loading required package: usethis</code></pre>
<pre class="r"><code>library(sjmisc)</code></pre>
<pre><code>## 
## Attaching package: 'sjmisc'</code></pre>
<pre><code>## The following objects are masked from 'package:jtools':
## 
##     %nin%, center</code></pre>
<pre class="r"><code>library(sjPlot)</code></pre>
<pre><code>## Registered S3 methods overwritten by 'lme4':
##   method                          from
##   cooks.distance.influence.merMod car 
##   influence.merMod                car 
##   dfbeta.influence.merMod         car 
##   dfbetas.influence.merMod        car</code></pre>
<pre class="r"><code>library(table1)</code></pre>
<pre><code>## 
## Attaching package: 'table1'</code></pre>
<pre><code>## The following objects are masked from 'package:base':
## 
##     units, units&lt;-</code></pre>
<pre class="r"><code>library(boot)</code></pre>
<pre><code>## 
## Attaching package: 'boot'</code></pre>
<pre><code>## The following object is masked from 'package:sm':
## 
##     dogs</code></pre>
<pre><code>## The following object is masked from 'package:psych':
## 
##     logit</code></pre>
<pre class="r"><code>library(expss)</code></pre>
<pre><code>## 
## Use 'expss_output_viewer()' to display tables in the RStudio Viewer.
##  To return to the console output, use 'expss_output_default()'.</code></pre>
<pre><code>## 
## Attaching package: 'expss'</code></pre>
<pre><code>## The following objects are masked from 'package:sjmisc':
## 
##     add_columns, add_rows, rec</code></pre>
<pre><code>## The following object is masked from 'package:ggpubr':
## 
##     compare_means</code></pre>
<pre><code>## The following objects are masked from 'package:pastecs':
## 
##     first, last</code></pre>
<pre><code>## The following object is masked from 'package:ggplot2':
## 
##     vars</code></pre>
<pre><code>## The following objects are masked from 'package:dplyr':
## 
##     between, compute, contains, first, last, na_if, recode, vars</code></pre>
<pre class="r"><code>library(summarytools)</code></pre>
<pre><code>## Registered S3 method overwritten by 'pryr':
##   method      from
##   print.bytes Rcpp</code></pre>
<pre><code>## For best results, restart R session and update pander using devtools:: or remotes::install_github('rapporter/pander')</code></pre>
<pre><code>## 
## Attaching package: 'summarytools'</code></pre>
<pre><code>## The following objects are masked from 'package:table1':
## 
##     label, label&lt;-</code></pre>
<pre><code>## The following object is masked from 'package:sjmisc':
## 
##     descr</code></pre>
<pre class="r"><code>library(tidyr)</code></pre>
<pre><code>## 
## Attaching package: 'tidyr'</code></pre>
<pre><code>## The following objects are masked from 'package:expss':
## 
##     contains, nest</code></pre>
<pre><code>## The following object is masked from 'package:sjmisc':
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##     replace_na</code></pre>
<pre><code>## The following object is masked from 'package:pastecs':
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##     extract</code></pre>
<pre class="r"><code># Read-in CCES module data (2018 and 2020 already pooled and stripped of identifying information)
cces &lt;- read.csv(&quot;cces.csv&quot;)

###########################################################################
###########################################################################
###########################################################################

# State Contextual Data #

context &lt;- read.csv(&quot;CCES_contextual_states.csv&quot;)
cces1 &lt;- merge(cces, context, by=c(&quot;inputstate&quot;), all=TRUE)
rm(context)

# MERGE IN CONGRESSIONAL DISTRICT DATA ##

cd &lt;- read.csv(&quot;cdid116_v2.csv&quot;, header = TRUE)
cd &lt;- subset(cd, select=c(state2, cdid116, biden2020, trump2020))

cd$cdid116 &lt;- as.numeric(cd$cdid116)
cces1$cdid116 &lt;- as.numeric(cces1$cdid116)

cces1 &lt;- merge(cces1, cd, by=c(&quot;state2&quot;, &quot;cdid116&quot;), all=TRUE)

# remove original CCES for housekeeping
rm(cces)
# &quot;cces1 is new baseline for data

# merge presidential vote data

load(&quot;~/Dropbox/Research/Proposals/MPSA 2019/countypres_2000-2016.RData&quot;)
#detach plyr to allow group_by functions to work
require(plyr) # package</code></pre>
<pre><code>## Loading required package: plyr</code></pre>
<pre><code>## ------------------------------------------------------------------------------</code></pre>
<pre><code>## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)</code></pre>
<pre><code>## ------------------------------------------------------------------------------</code></pre>
<pre><code>## 
## Attaching package: 'plyr'</code></pre>
<pre><code>## The following object is masked from 'package:expss':
## 
##     split_labels</code></pre>
<pre><code>## The following objects are masked from 'package:rstatix':
## 
##     desc, mutate</code></pre>
<pre><code>## The following object is masked from 'package:ggpubr':
## 
##     mutate</code></pre>
<pre><code>## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize</code></pre>
<pre class="r"><code>detach(package:plyr)

presvote &lt;- x
rm(x)
presvote &lt;- subset(presvote, year==2016)
presvote &lt;- subset(presvote, party!=&quot;NA&quot;)

presvote2party &lt;- presvote %&gt;% 
  group_by(FIPS, year) %&gt;% 
  summarise(Frequency = sum(candidatevotes, na.rm=TRUE))</code></pre>
<pre><code>## `summarise()` regrouping output by 'FIPS' (override with `.groups` argument)</code></pre>
<pre class="r"><code>colnames(presvote2party) &lt;- c(&quot;FIPS&quot;,&quot;year&quot;,&quot;presvotes_2party&quot;)

presvote &lt;- merge(presvote, presvote2party, by = c(&quot;FIPS&quot;, &quot;year&quot;))
rm(presvote2party)

presvote$voteshare2party &lt;- (presvote$candidatevotes/presvote$presvotes_2party)*100

presvote$d_voteshare2party = ifelse(presvote$party == &quot;democrat&quot;, presvote$voteshare2party, NA)
presvote$r_voteshare2party = ifelse(presvote$party == &quot;republican&quot;, presvote$voteshare2party, NA)
presvote_d &lt;- subset(presvote, d_voteshare2party!=&quot;NA&quot;)
presvote_r &lt;- subset(presvote, r_voteshare2party!=&quot;NA&quot;)

presvote_d &lt;- presvote_d[c(-15)]
presvote_r &lt;- presvote_r[c(-14)]

## Just 2016 vote share by county - democrats ##
avg_dvote &lt;- presvote_d %&gt;% 
  group_by(FIPS) %&gt;% 
  summarise(Frequency = sum(d_voteshare2party))</code></pre>
<pre><code>## `summarise()` ungrouping output (override with `.groups` argument)</code></pre>
<pre class="r"><code>colnames(avg_dvote) &lt;- c(&quot;FIPS&quot;,&quot;avg_d_voteshare2party&quot;)

avg_presvote_d &lt;- merge(presvote_d, avg_dvote, by = c(&quot;FIPS&quot;))

avg_presvote_d &lt;- subset(avg_presvote_d, select=c(FIPS, state, state_po, county, avg_d_voteshare2party))

rm(avg_dvote)
rm(presvote_d)

## just 2016 - republicans ##
avg_rvote &lt;- presvote_r %&gt;% 
  group_by(FIPS) %&gt;% 
  summarise(Frequency = sum(r_voteshare2party))</code></pre>
<pre><code>## `summarise()` ungrouping output (override with `.groups` argument)</code></pre>
<pre class="r"><code>colnames(avg_rvote) &lt;- c(&quot;FIPS&quot;,&quot;avg_r_voteshare2party&quot;)

avg_presvote_r &lt;- merge(presvote_r, avg_rvote, by = c(&quot;FIPS&quot;))

avg_presvote_r &lt;- subset(avg_presvote_r, select=c(FIPS, state, state_po, county, avg_r_voteshare2party))

rm(avg_rvote)
rm(presvote_r)

##

avg_dvote &lt;- subset(avg_presvote_d, select=c(FIPS, state, state_po, county, avg_d_voteshare2party))
avg_rvote &lt;- subset(avg_presvote_r, select=c(FIPS, avg_r_voteshare2party))

presvote &lt;- presvote[c(-14, -15)]
presvote &lt;- merge(avg_dvote, avg_rvote, by = c(&quot;FIPS&quot;))

rm(avg_dvote)
rm(avg_rvote)
rm(avg_presvote_d)
rm(avg_presvote_r)

# add leading zero to FIPS in presvote data
library(stringr)</code></pre>
<pre><code>## 
## Attaching package: 'stringr'</code></pre>
<pre><code>## The following objects are masked from 'package:expss':
## 
##     fixed, regex</code></pre>
<pre class="r"><code>presvote$FIPS &lt;- str_pad(presvote$FIPS, 5, pad = &quot;0&quot;)
presvote$FIPS &lt;- as.integer((presvote$FIPS))
typeof(presvote$FIPS)</code></pre>
<pre><code>## [1] &quot;integer&quot;</code></pre>
<pre class="r"><code>cces1$FIPS &lt;- cces1$countyfips

## New Dataset - 'cces2' (before merging, keep cces1 as is)
## But first - ensure that everything gets merged, even if has some missing data

library(tidyr)

cces2 &lt;- merge(cces1, presvote, by=&quot;FIPS&quot;, all = FALSE)
cces2 &lt;- cces2 %&gt;% drop_na(state2)

rm(presvote)

# Create County Partisanship Indicator #

cces2$Pres_Vote &lt;- ifelse(cces2$avg_r_voteshare2party &gt;=65, &quot;Solid Republican&quot;,
                          ifelse(cces2$avg_r_voteshare2party &gt;=50 &amp; cces2$avg_r_voteshare2party &lt; 65, &quot;Lean Republican&quot;,
                                 ifelse(cces2$avg_r_voteshare2party &lt;50 &amp; cces2$avg_r_voteshare2party &gt;=35, &quot;Lean Democrat&quot;,
                                        ifelse(cces2$avg_r_voteshare2party &lt;35, &quot;Solid Democrat&quot;, NA))))

###################################
## Exclude True Independents ##
cces2.2 &lt;- subset(cces2, pid!=&quot;Independent&quot;)

## Drop Independent Factor Level
cces2.2$pid &lt;- droplevels(cces2.2$pid) 

table(cces2.2$pid)</code></pre>
<pre><code>## 
##   Democrat Republican 
##        945        712</code></pre>
<pre class="r"><code>## Subsets specifically for Margins and Plotting (custom labelling)

# School Board Margins Plots #

sbMargins &lt;- cces2.2

sbMarginsNP &lt;- c(&quot;RCO322_np&quot;, &quot;avg_d_voteshare2party&quot;, &quot;pid&quot;,
                 &quot;perceive_extreme_very&quot;, &quot;polint&quot;, &quot;educ.int&quot;,
                 &quot;birthyr&quot;, &quot;Pres_Vote&quot;, &quot;biden2020&quot;, &quot;trump2020&quot;)

sbMarginsNP2 &lt;- sbMargins[sbMarginsNP]

#Ensure factors for glm

sbMarginsNP2$pid.f &lt;- as.factor(sbMarginsNP2$pid)
sbMarginsNP2$pid.f &lt;- relevel(sbMarginsNP2$pid.f, ref = &quot;Republican&quot;)
#
sbMarginsNP2$polint.f &lt;- as.factor(sbMarginsNP2$polint)
#
sbMarginsNP2$perceive_extreme_very.f &lt;- as.factor(sbMarginsNP2$perceive_extreme_very)
#
sbMarginsNP2$educ.int.f &lt;- as.factor(sbMarginsNP2$educ.int)
#
sbMarginsNP2$Pres_Vote.f &lt;- as.factor(sbMarginsNP2$Pres_Vote)
sbMarginsNP2$Pres_Vote.f &lt;- factor(sbMarginsNP2$Pres_Vote.f, levels = c(&quot;Solid Democrat&quot;, 
                                                                        &quot;Lean Democrat&quot;, 
                                                                        &quot;Lean Republican&quot;,
                                                                        &quot;Solid Republican&quot;))

sbMarginsNP2$Pres_Vote.f &lt;- relevel(sbMarginsNP2$Pres_Vote.f, ref = &quot;Solid Democrat&quot;)

# LEO Margins Plots #

leoMargins &lt;- cces2.2

leoMarginsNP &lt;- c(&quot;RCO321_np&quot;, &quot;avg_d_voteshare2party&quot;, &quot;pid&quot;,
                  &quot;perceive_extreme_very&quot;, &quot;polint&quot;, &quot;educ.int&quot;,
                  &quot;birthyr&quot;, &quot;Pres_Vote&quot;, &quot;biden2020&quot;, &quot;trump2020&quot;)

leoMarginsNP2 &lt;- leoMargins[leoMarginsNP]

#Ensure factors for glm

leoMarginsNP2$pid.f &lt;- as.factor(leoMarginsNP2$pid)
leoMarginsNP2$pid.f &lt;- relevel(leoMarginsNP2$pid.f, ref = &quot;Republican&quot;)
#
leoMarginsNP2$polint.f &lt;- as.factor(leoMarginsNP2$polint)
#
leoMarginsNP2$perceive_extreme_very.f &lt;- as.factor(leoMarginsNP2$perceive_extreme_very)
#
leoMarginsNP2$educ.int.f &lt;- as.factor(leoMarginsNP2$educ.int)
#
leoMarginsNP2$Pres_Vote.f &lt;- as.factor(leoMarginsNP2$Pres_Vote)
leoMarginsNP2$Pres_Vote.f &lt;- factor(leoMarginsNP2$Pres_Vote.f, levels = c(&quot;Solid Democrat&quot;, 
                                                                          &quot;Lean Democrat&quot;, 
                                                                          &quot;Lean Republican&quot;,
                                                                          &quot;Solid Republican&quot;))

leoMarginsNP2$Pres_Vote.f &lt;- relevel(leoMarginsNP2$Pres_Vote.f, ref = &quot;Solid Democrat&quot;)

#######

# Sheriff Margins Plots #

sherMargins &lt;- cces2.2

sherMarginsNP &lt;- c(&quot;RCO323_np&quot;, &quot;avg_d_voteshare2party&quot;, &quot;pid&quot;,
                   &quot;perceive_extreme_very&quot;, &quot;polint&quot;, &quot;educ.int&quot;,
                   &quot;birthyr&quot;, &quot;Pres_Vote&quot;, &quot;biden2020&quot;, &quot;trump2020&quot;)

sherMarginsNP2 &lt;- sherMargins[sherMarginsNP]

#Ensure factors for glm

sherMarginsNP2$pid.f &lt;- as.factor(sherMarginsNP2$pid)
sherMarginsNP2$pid.f &lt;- relevel(sherMarginsNP2$pid.f, ref = &quot;Republican&quot;)
#
sherMarginsNP2$polint.f &lt;- as.factor(sherMarginsNP2$polint)
#
sherMarginsNP2$perceive_extreme_very.f &lt;- as.factor(sherMarginsNP2$perceive_extreme_very)
#
sherMarginsNP2$educ.int.f &lt;- as.factor(sherMarginsNP2$educ.int)
#
sherMarginsNP2$Pres_Vote.f &lt;- as.factor(sherMarginsNP2$Pres_Vote)
sherMarginsNP2$Pres_Vote.f &lt;- factor(sherMarginsNP2$Pres_Vote.f, levels = c(&quot;Solid Democrat&quot;, 
                                                                            &quot;Lean Democrat&quot;, 
                                                                            &quot;Lean Republican&quot;,
                                                                            &quot;Solid Republican&quot;))

sherMarginsNP2$Pres_Vote.f &lt;- relevel(sherMarginsNP2$Pres_Vote.f, ref = &quot;Solid Democrat&quot;)


#######

# DA Margins Plots #

daMargins &lt;- cces2.2

daMarginsNP &lt;- c(&quot;RCO324_np&quot;, &quot;avg_d_voteshare2party&quot;, &quot;pid&quot;,
                 &quot;perceive_extreme_very&quot;, &quot;polint&quot;, &quot;educ.int&quot;,
                 &quot;birthyr&quot;, &quot;Pres_Vote&quot;, &quot;biden2020&quot;, &quot;trump2020&quot;)

daMarginsNP2 &lt;- daMargins[daMarginsNP]

#Ensure factors for glm

daMarginsNP2$pid.f &lt;- as.factor(daMarginsNP2$pid)
daMarginsNP2$pid.f &lt;- relevel(daMarginsNP2$pid.f, ref = &quot;Republican&quot;)
#
daMarginsNP2$polint.f &lt;- as.factor(daMarginsNP2$polint)
#
daMarginsNP2$perceive_extreme_very.f &lt;- as.factor(daMarginsNP2$perceive_extreme_very)
#
daMarginsNP2$educ.int.f &lt;- as.factor(daMarginsNP2$educ.int)
#
daMarginsNP2$Pres_Vote.f &lt;- as.factor(daMarginsNP2$Pres_Vote)
daMarginsNP2$Pres_Vote.f &lt;- factor(daMarginsNP2$Pres_Vote.f, levels = c(&quot;Solid Democrat&quot;, 
                                                                        &quot;Lean Democrat&quot;, 
                                                                        &quot;Lean Republican&quot;,
                                                                        &quot;Solid Republican&quot;))

daMarginsNP2$Pres_Vote.f &lt;- relevel(daMarginsNP2$Pres_Vote.f, ref = &quot;Solid Democrat&quot;)

#######

# House Margins Plots #

houseMargins &lt;- cces2.2

houseMarginsNP &lt;- c(&quot;RCO327_np&quot;, &quot;avg_d_voteshare2party&quot;, &quot;pid&quot;,
                    &quot;perceive_extreme_very&quot;, &quot;polint&quot;, &quot;educ.int&quot;,
                    &quot;birthyr&quot;, &quot;Pres_Vote&quot;, &quot;biden2020&quot;, &quot;trump2020&quot;)

houseMarginsNP2 &lt;- daMargins[houseMarginsNP]

#Ensure factors for glm

houseMarginsNP2$pid.f &lt;- as.factor(houseMarginsNP2$pid)
houseMarginsNP2$pid.f &lt;- relevel(houseMarginsNP2$pid.f, ref = &quot;Republican&quot;)
#
houseMarginsNP2$polint.f &lt;- as.factor(houseMarginsNP2$polint)
#
houseMarginsNP2$perceive_extreme_very.f &lt;- as.factor(houseMarginsNP2$perceive_extreme_very)
#
houseMarginsNP2$educ.int.f &lt;- as.factor(houseMarginsNP2$educ.int)
#
houseMarginsNP2$Pres_Vote.f &lt;- as.factor(houseMarginsNP2$Pres_Vote)
houseMarginsNP2$Pres_Vote.f &lt;- factor(houseMarginsNP2$Pres_Vote.f, levels = c(&quot;Solid Democrat&quot;, 
                                                                              &quot;Lean Democrat&quot;, 
                                                                              &quot;Lean Republican&quot;,
                                                                              &quot;Solid Republican&quot;))

houseMarginsNP2$Pres_Vote.f &lt;- relevel(houseMarginsNP2$Pres_Vote.f, ref = &quot;Solid Democrat&quot;)

#####################################################################

## Subsets specifically for Regression Outputs ##
library(stargazer)
library(sjPlot)
library(sjmisc)
library(sjlabelled)</code></pre>
<pre><code>## 
## Attaching package: 'sjlabelled'</code></pre>
<pre><code>## The following object is masked from 'package:summarytools':
## 
##     unlabel</code></pre>
<pre><code>## The following object is masked from 'package:expss':
## 
##     read_spss</code></pre>
<pre><code>## The following object is masked from 'package:usethis':
## 
##     tidy_labels</code></pre>
<pre><code>## The following object is masked from 'package:dplyr':
## 
##     as_label</code></pre>
<pre class="r"><code>library(glmmTMB)
library(Hmisc)</code></pre>
<pre><code>## Loading required package: lattice</code></pre>
<pre><code>## 
## Attaching package: 'lattice'</code></pre>
<pre><code>## The following object is masked from 'package:boot':
## 
##     melanoma</code></pre>
<pre><code>## Loading required package: survival</code></pre>
<pre><code>## 
## Attaching package: 'survival'</code></pre>
<pre><code>## The following object is masked from 'package:boot':
## 
##     aml</code></pre>
<pre><code>## Loading required package: Formula</code></pre>
<pre><code>## Registered S3 methods overwritten by 'Hmisc':
##   method                 from 
##   [.labelled             expss
##   print.labelled         expss
##   as.data.frame.labelled expss</code></pre>
<pre><code>## 
## Attaching package: 'Hmisc'</code></pre>
<pre><code>## The following objects are masked from 'package:summarytools':
## 
##     label, label&lt;-</code></pre>
<pre><code>## The following objects are masked from 'package:table1':
## 
##     label, label&lt;-, units</code></pre>
<pre><code>## The following object is masked from 'package:sjmisc':
## 
##     %nin%</code></pre>
<pre><code>## The following object is masked from 'package:jtools':
## 
##     %nin%</code></pre>
<pre><code>## The following object is masked from 'package:psych':
## 
##     describe</code></pre>
<pre><code>## The following objects are masked from 'package:dplyr':
## 
##     src, summarize</code></pre>
<pre><code>## The following objects are masked from 'package:base':
## 
##     format.pval, units</code></pre>
<pre class="r"><code>library(glmmTMB)

###################################################

## School Board ##

sbGLM &lt;- cces2.2

sbGLM$pid.f &lt;- as.factor(sbGLM$pid)
sbGLM$polint.f &lt;- as.factor(sbGLM$polint)
sbGLM$perceive_extreme_very.f &lt;- as.factor(sbGLM$perceive_extreme_very)
sbGLM$educ.int.f &lt;- as.factor(sbGLM$educ.int)
sbGLM$Pres_Vote.f &lt;- as.factor(sbGLM$Pres_Vote)

## Change Labels ##

sbGLM$polint.f &lt;- 
  factor(sbGLM$polint.f, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;),
         labels=c(&quot;News: Hardly at all&quot;, 
                  &quot;News: Now and then&quot;,
                  &quot;News: Some of the time&quot;,
                  &quot;News: Most of the time&quot;))

sbGLM$perceive_extreme_very.f &lt;- 
  factor(sbGLM$perceive_extreme_very.f, levels=c(&quot;0&quot;,&quot;1&quot;),
         labels=c(&quot;Not extreme&quot;, 
                  &quot;Outparty is extreme&quot;))

sbGLM$educ.int.f &lt;- 
  factor(sbGLM$educ.int, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;,&quot;5&quot;),
         labels=c(&quot;H.S. or less&quot;, 
                  &quot;Some College&quot;,
                  &quot;2-Year Degree&quot;,
                  &quot;4-Year Degree&quot;,
                  &quot;Post-Graduate&quot;))

sbGLM$Pres_Vote.f &lt;- 
  factor(sbGLM$Pres_Vote.f, 
         levels = c(&quot;Solid Democrat&quot;, 
                    &quot;Lean Democrat&quot;,
                    &quot;Lean Republican&quot;,
                    &quot;Solid Republican&quot;),
         labels=c(&quot;Solid Democrat&quot;, 
                  &quot;Lean Democrat&quot;,
                  &quot;Lean Republican&quot;,
                  &quot;Solid Republican&quot;))


label(sbGLM$pid.f) &lt;- &quot;Party&quot;
label(sbGLM$RCO322_np) &lt;- &quot;Preference for Nonpartisan School Board&quot;
label(sbGLM$Pres_Vote.f) &lt;- &quot;County 2016 Pres. Vote&quot;
label(sbGLM$educ.int.f) &lt;- &quot;Education&quot;
label(sbGLM$perceive_extreme_very.f) &lt;- &quot;Outparty Extremity&quot;
label(sbGLM$polint.f) &lt;- &quot;News Interest&quot;


###################################################

## LEO ##

leoGLM &lt;- cces2.2

leoGLM$pid.f &lt;- as.factor(leoGLM$pid)
leoGLM$polint.f &lt;- as.factor(leoGLM$polint)
leoGLM$perceive_extreme_very.f &lt;- as.factor(leoGLM$perceive_extreme_very)
leoGLM$educ.int.f &lt;- as.factor(leoGLM$educ.int)
leoGLM$Pres_Vote.f &lt;- as.factor(leoGLM$Pres_Vote)

## Change Labels ##

leoGLM$polint.f &lt;- 
  factor(leoGLM$polint.f, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;),
         labels=c(&quot;News: Hardly at all&quot;, 
                  &quot;News: Now and then&quot;,
                  &quot;News: Some of the time&quot;,
                  &quot;News: Most of the time&quot;))

leoGLM$perceive_extreme_very.f &lt;- 
  factor(leoGLM$perceive_extreme_very.f, levels=c(&quot;0&quot;,&quot;1&quot;),
         labels=c(&quot;Not extreme&quot;, 
                  &quot;Outparty is extreme&quot;))

leoGLM$educ.int.f &lt;- 
  factor(leoGLM$educ.int, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;,&quot;5&quot;),
         labels=c(&quot;H.S. or less&quot;, 
                  &quot;Some College&quot;,
                  &quot;2-Year Degree&quot;,
                  &quot;4-Year Degree&quot;,
                  &quot;Post-Graduate&quot;))

leoGLM$Pres_Vote.f &lt;- 
  factor(leoGLM$Pres_Vote.f, 
         levels = c(&quot;Solid Democrat&quot;, 
                    &quot;Lean Democrat&quot;,
                    &quot;Lean Republican&quot;,
                    &quot;Solid Republican&quot;),
         labels=c(&quot;Solid Democrat&quot;, 
                  &quot;Lean Democrat&quot;,
                  &quot;Lean Republican&quot;,
                  &quot;Solid Republican&quot;))


label(leoGLM$pid.f) &lt;- &quot;Party&quot;
label(leoGLM$RCO321_np) &lt;- &quot;Preference for Nonpartisan Local Election Official&quot;
label(leoGLM$Pres_Vote.f) &lt;- &quot;County 2016 Pres. Vote&quot;
label(leoGLM$educ.int.f) &lt;- &quot;Education&quot;
label(leoGLM$perceive_extreme_very.f) &lt;- &quot;Outparty Extremity&quot;
label(leoGLM$polint.f) &lt;- &quot;News Interest&quot;



###################################################

## Sheriff ##

sherGLM &lt;- cces2.2

sherGLM$pid.f &lt;- as.factor(sherGLM$pid)
sherGLM$polint.f &lt;- as.factor(sherGLM$polint)
sherGLM$perceive_extreme_very.f &lt;- as.factor(sherGLM$perceive_extreme_very)
sherGLM$educ.int.f &lt;- as.factor(sherGLM$educ.int)
sherGLM$Pres_Vote.f &lt;- as.factor(sherGLM$Pres_Vote)

## Change Labels ##

sherGLM$polint.f &lt;- 
  factor(sherGLM$polint.f, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;),
         labels=c(&quot;News: Hardly at all&quot;, 
                  &quot;News: Now and then&quot;,
                  &quot;News: Some of the time&quot;,
                  &quot;News: Most of the time&quot;))

sherGLM$perceive_extreme_very.f &lt;- 
  factor(sherGLM$perceive_extreme_very.f, levels=c(&quot;0&quot;,&quot;1&quot;),
         labels=c(&quot;Not extreme&quot;, 
                  &quot;Outparty is extreme&quot;))

sherGLM$educ.int.f &lt;- 
  factor(sherGLM$educ.int, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;,&quot;5&quot;),
         labels=c(&quot;H.S. or less&quot;, 
                  &quot;Some College&quot;,
                  &quot;2-Year Degree&quot;,
                  &quot;4-Year Degree&quot;,
                  &quot;Post-Graduate&quot;))

sherGLM$Pres_Vote.f &lt;- 
  factor(sherGLM$Pres_Vote.f, 
         levels = c(&quot;Solid Democrat&quot;, 
                    &quot;Lean Democrat&quot;,
                    &quot;Lean Republican&quot;,
                    &quot;Solid Republican&quot;),
         labels=c(&quot;Solid Democrat&quot;, 
                  &quot;Lean Democrat&quot;,
                  &quot;Lean Republican&quot;,
                  &quot;Solid Republican&quot;))


label(sherGLM$pid.f) &lt;- &quot;Party&quot;
label(sherGLM$RCO323_np) &lt;- &quot;Preference for Nonpartisan Sheriff&quot;
label(sherGLM$Pres_Vote.f) &lt;- &quot;County 2016 Pres. Vote&quot;
label(sherGLM$educ.int.f) &lt;- &quot;Education&quot;
label(sherGLM$perceive_extreme_very.f) &lt;- &quot;Outparty Extremity&quot;
label(sherGLM$polint.f) &lt;- &quot;News Interest&quot;


###################################################

## DA ##

daGLM &lt;- cces2.2

daGLM$pid.f &lt;- as.factor(daGLM$pid)
daGLM$polint.f &lt;- as.factor(daGLM$polint)
daGLM$perceive_extreme_very.f &lt;- as.factor(daGLM$perceive_extreme_very)
daGLM$educ.int.f &lt;- as.factor(daGLM$educ.int)
daGLM$Pres_Vote.f &lt;- as.factor(daGLM$Pres_Vote)

## Change Labels ##

daGLM$polint.f &lt;- 
  factor(daGLM$polint.f, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;),
         labels=c(&quot;News: Hardly at all&quot;, 
                  &quot;News: Now and then&quot;,
                  &quot;News: Some of the time&quot;,
                  &quot;News: Most of the time&quot;))

daGLM$perceive_extreme_very.f &lt;- 
  factor(daGLM$perceive_extreme_very.f, levels=c(&quot;0&quot;,&quot;1&quot;),
         labels=c(&quot;Not extreme&quot;, 
                  &quot;Outparty is extreme&quot;))

daGLM$educ.int.f &lt;- 
  factor(daGLM$educ.int, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;,&quot;5&quot;),
         labels=c(&quot;H.S. or less&quot;, 
                  &quot;Some College&quot;,
                  &quot;2-Year Degree&quot;,
                  &quot;4-Year Degree&quot;,
                  &quot;Post-Graduate&quot;))

daGLM$Pres_Vote.f &lt;- 
  factor(daGLM$Pres_Vote.f, 
         levels = c(&quot;Solid Democrat&quot;, 
                    &quot;Lean Democrat&quot;,
                    &quot;Lean Republican&quot;,
                    &quot;Solid Republican&quot;),
         labels=c(&quot;Solid Democrat&quot;, 
                  &quot;Lean Democrat&quot;,
                  &quot;Lean Republican&quot;,
                  &quot;Solid Republican&quot;))


label(daGLM$pid.f) &lt;- &quot;Party&quot;
label(daGLM$RCO324_np) &lt;- &quot;Preference for Nonpartisan District Attorney&quot;
label(daGLM$Pres_Vote.f) &lt;- &quot;County 2016 Pres. Vote&quot;
label(daGLM$educ.int.f) &lt;- &quot;Education&quot;
label(daGLM$perceive_extreme_very.f) &lt;- &quot;Outparty Extremity&quot;
label(daGLM$polint.f) &lt;- &quot;News Interest&quot;


###################################################

## U.S. House ##

houseGLM &lt;- cces2.2

houseGLM$pid.f &lt;- as.factor(houseGLM$pid)
houseGLM$polint.f &lt;- as.factor(houseGLM$polint)
houseGLM$perceive_extreme_very.f &lt;- as.factor(houseGLM$perceive_extreme_very)
houseGLM$educ.int.f &lt;- as.factor(houseGLM$educ.int)
houseGLM$Pres_Vote.f &lt;- as.factor(houseGLM$Pres_Vote)

## Change Labels ##

houseGLM$polint.f &lt;- 
  factor(houseGLM$polint.f, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;),
         labels=c(&quot;News: Hardly at all&quot;, 
                  &quot;News: Now and then&quot;,
                  &quot;News: Some of the time&quot;,
                  &quot;News: Most of the time&quot;))

houseGLM$perceive_extreme_very.f &lt;- 
  factor(houseGLM$perceive_extreme_very.f, levels=c(&quot;0&quot;,&quot;1&quot;),
         labels=c(&quot;Not extreme&quot;, 
                  &quot;Outparty is extreme&quot;))

houseGLM$educ.int.f &lt;- 
  factor(houseGLM$educ.int, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;,&quot;5&quot;),
         labels=c(&quot;H.S. or less&quot;, 
                  &quot;Some College&quot;,
                  &quot;2-Year Degree&quot;,
                  &quot;4-Year Degree&quot;,
                  &quot;Post-Graduate&quot;))

houseGLM$Pres_Vote.f &lt;- 
  factor(houseGLM$Pres_Vote.f, 
         levels = c(&quot;Solid Democrat&quot;, 
                    &quot;Lean Democrat&quot;,
                    &quot;Lean Republican&quot;,
                    &quot;Solid Republican&quot;),
         labels=c(&quot;Solid Democrat&quot;, 
                  &quot;Lean Democrat&quot;,
                  &quot;Lean Republican&quot;,
                  &quot;Solid Republican&quot;))


label(houseGLM$pid.f) &lt;- &quot;Party&quot;
label(houseGLM$RCO327_np) &lt;- &quot;Preference for Nonpartisan U.S. House&quot;
label(houseGLM$Pres_Vote.f) &lt;- &quot;County 2016 Pres. Vote&quot;
label(houseGLM$educ.int.f) &lt;- &quot;Education&quot;
label(houseGLM$perceive_extreme_very.f) &lt;- &quot;Outparty Extremity&quot;
label(houseGLM$polint.f) &lt;- &quot;News Interest&quot;

###################################

## PARTISAN AS THE DV ##

## Subsets specifically for Margins and Plotting (custom labelling)

# School Board Margins Plots #

sbMargins &lt;- cces2.2

sbMarginsP &lt;- c(&quot;RCO322_p&quot;, &quot;avg_d_voteshare2party&quot;, &quot;pid&quot;,
                &quot;perceive_extreme_very&quot;, &quot;polint&quot;, &quot;educ.int&quot;,
                &quot;birthyr&quot;, &quot;Pres_Vote&quot;, &quot;biden2020&quot;, &quot;trump2020&quot;)

sbMarginsP2 &lt;- sbMargins[sbMarginsP]

#Ensure factors for glm

sbMarginsP2$pid.f &lt;- as.factor(sbMarginsP2$pid)
sbMarginsP2$pid.f &lt;- relevel(sbMarginsP2$pid.f, ref = &quot;Republican&quot;)
#
sbMarginsP2$polint.f &lt;- as.factor(sbMarginsP2$polint)
#
sbMarginsP2$perceive_extreme_very.f &lt;- as.factor(sbMarginsP2$perceive_extreme_very)
#
sbMarginsP2$educ.int.f &lt;- as.factor(sbMarginsP2$educ.int)
#
sbMarginsP2$Pres_Vote.f &lt;- as.factor(sbMarginsP2$Pres_Vote)
sbMarginsP2$Pres_Vote.f &lt;- factor(sbMarginsP2$Pres_Vote.f, levels = c(&quot;Solid Democrat&quot;, 
                                                                      &quot;Lean Democrat&quot;, 
                                                                      &quot;Lean Republican&quot;,
                                                                      &quot;Solid Republican&quot;))

sbMarginsP2$Pres_Vote.f &lt;- relevel(sbMarginsP2$Pres_Vote.f, ref = &quot;Solid Democrat&quot;)

# LEO Margins Plots #

leoMargins &lt;- cces2.2

leoMarginsP &lt;- c(&quot;RCO321_p&quot;, &quot;avg_d_voteshare2party&quot;, &quot;pid&quot;,
                 &quot;perceive_extreme_very&quot;, &quot;polint&quot;, &quot;educ.int&quot;,
                 &quot;birthyr&quot;, &quot;Pres_Vote&quot;, &quot;biden2020&quot;, &quot;trump2020&quot;)

leoMarginsP2 &lt;- leoMargins[leoMarginsP]

#Ensure factors for glm

leoMarginsP2$pid.f &lt;- as.factor(leoMarginsP2$pid)
leoMarginsP2$pid.f &lt;- relevel(leoMarginsP2$pid.f, ref = &quot;Republican&quot;)
#
leoMarginsP2$polint.f &lt;- as.factor(leoMarginsP2$polint)
#
leoMarginsP2$perceive_extreme_very.f &lt;- as.factor(leoMarginsP2$perceive_extreme_very)
#
leoMarginsP2$educ.int.f &lt;- as.factor(leoMarginsP2$educ.int)
#
leoMarginsP2$Pres_Vote.f &lt;- as.factor(leoMarginsP2$Pres_Vote)
leoMarginsP2$Pres_Vote.f &lt;- factor(leoMarginsP2$Pres_Vote.f, levels = c(&quot;Solid Democrat&quot;, 
                                                                        &quot;Lean Democrat&quot;, 
                                                                        &quot;Lean Republican&quot;,
                                                                        &quot;Solid Republican&quot;))

leoMarginsP2$Pres_Vote.f &lt;- relevel(leoMarginsP2$Pres_Vote.f, ref = &quot;Solid Democrat&quot;)

#######

# Sheriff Margins Plots #

sherMargins &lt;- cces2.2

sherMarginsP &lt;- c(&quot;RCO323_p&quot;, &quot;avg_d_voteshare2party&quot;, &quot;pid&quot;,
                  &quot;perceive_extreme_very&quot;, &quot;polint&quot;, &quot;educ.int&quot;,
                  &quot;birthyr&quot;, &quot;Pres_Vote&quot;, &quot;biden2020&quot;, &quot;trump2020&quot;)

sherMarginsP2 &lt;- sherMargins[sherMarginsP]

#Ensure factors for glm

sherMarginsP2$pid.f &lt;- as.factor(sherMarginsP2$pid)
sherMarginsP2$pid.f &lt;- relevel(sherMarginsP2$pid.f, ref = &quot;Republican&quot;)
#
sherMarginsP2$polint.f &lt;- as.factor(sherMarginsP2$polint)
#
sherMarginsP2$perceive_extreme_very.f &lt;- as.factor(sherMarginsP2$perceive_extreme_very)
#
sherMarginsP2$educ.int.f &lt;- as.factor(sherMarginsP2$educ.int)
#
sherMarginsP2$Pres_Vote.f &lt;- as.factor(sherMarginsP2$Pres_Vote)
sherMarginsP2$Pres_Vote.f &lt;- factor(sherMarginsP2$Pres_Vote.f, levels = c(&quot;Solid Democrat&quot;, 
                                                                          &quot;Lean Democrat&quot;, 
                                                                          &quot;Lean Republican&quot;,
                                                                          &quot;Solid Republican&quot;))

sherMarginsP2$Pres_Vote.f &lt;- relevel(sherMarginsP2$Pres_Vote.f, ref = &quot;Solid Democrat&quot;)


#######

# DA Margins Plots #

daMargins &lt;- cces2.2

daMarginsP &lt;- c(&quot;RCO324_p&quot;, &quot;avg_d_voteshare2party&quot;, &quot;pid&quot;,
                &quot;perceive_extreme_very&quot;, &quot;polint&quot;, &quot;educ.int&quot;,
                &quot;birthyr&quot;, &quot;Pres_Vote&quot;, &quot;biden2020&quot;, &quot;trump2020&quot;)

daMarginsP2 &lt;- daMargins[daMarginsP]

#Ensure factors for glm

daMarginsP2$pid.f &lt;- as.factor(daMarginsP2$pid)
daMarginsP2$pid.f &lt;- relevel(daMarginsP2$pid.f, ref = &quot;Republican&quot;)
#
daMarginsP2$polint.f &lt;- as.factor(daMarginsP2$polint)
#
daMarginsP2$perceive_extreme_very.f &lt;- as.factor(daMarginsP2$perceive_extreme_very)
#
daMarginsP2$educ.int.f &lt;- as.factor(daMarginsP2$educ.int)
#
daMarginsP2$Pres_Vote.f &lt;- as.factor(daMarginsP2$Pres_Vote)
daMarginsP2$Pres_Vote.f &lt;- factor(daMarginsP2$Pres_Vote.f, levels = c(&quot;Solid Democrat&quot;, 
                                                                      &quot;Lean Democrat&quot;, 
                                                                      &quot;Lean Republican&quot;,
                                                                      &quot;Solid Republican&quot;))

daMarginsP2$Pres_Vote.f &lt;- relevel(daMarginsP2$Pres_Vote.f, ref = &quot;Solid Democrat&quot;)

#######

# House Margins Plots #

houseMargins &lt;- cces2.2

houseMarginsP &lt;- c(&quot;RCO327_p&quot;, &quot;avg_d_voteshare2party&quot;, &quot;pid&quot;,
                   &quot;perceive_extreme_very&quot;, &quot;polint&quot;, &quot;educ.int&quot;,
                   &quot;birthyr&quot;, &quot;Pres_Vote&quot;, &quot;biden2020&quot;, &quot;trump2020&quot;)

houseMarginsP2 &lt;- daMargins[houseMarginsP]

#Ensure factors for glm

houseMarginsP2$pid.f &lt;- as.factor(houseMarginsP2$pid)
houseMarginsP2$pid.f &lt;- relevel(houseMarginsP2$pid.f, ref = &quot;Republican&quot;)
#
houseMarginsP2$polint.f &lt;- as.factor(houseMarginsP2$polint)
#
houseMarginsP2$perceive_extreme_very.f &lt;- as.factor(houseMarginsP2$perceive_extreme_very)
#
houseMarginsP2$educ.int.f &lt;- as.factor(houseMarginsP2$educ.int)
#
houseMarginsP2$Pres_Vote.f &lt;- as.factor(houseMarginsP2$Pres_Vote)
houseMarginsP2$Pres_Vote.f &lt;- factor(houseMarginsP2$Pres_Vote.f, levels = c(&quot;Solid Democrat&quot;, 
                                                                            &quot;Lean Democrat&quot;, 
                                                                            &quot;Lean Republican&quot;,
                                                                            &quot;Solid Republican&quot;))

houseMarginsP2$Pres_Vote.f &lt;- relevel(houseMarginsP2$Pres_Vote.f, ref = &quot;Solid Democrat&quot;)

#####################################################################

## Subsets specifically for Regression Outputs ##
library(stargazer)
library(sjPlot)
library(sjmisc)
library(sjlabelled)
library(glmmTMB)
library(Hmisc)
library(glmmTMB)

###################################################

## School Board ##

sbGLMp &lt;- cces2.2

sbGLMp$pid.f &lt;- as.factor(sbGLMp$pid)
sbGLMp$polint.f &lt;- as.factor(sbGLMp$polint)
sbGLMp$perceive_extreme_very.f &lt;- as.factor(sbGLMp$perceive_extreme_very)
sbGLMp$educ.int.f &lt;- as.factor(sbGLMp$educ.int)
sbGLMp$Pres_Vote.f &lt;- as.factor(sbGLMp$Pres_Vote)

## Change Labels ##

sbGLMp$polint.f &lt;- 
  factor(sbGLMp$polint.f, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;),
         labels=c(&quot;News: Hardly at all&quot;, 
                  &quot;News: Now and then&quot;,
                  &quot;News: Some of the time&quot;,
                  &quot;News: Most of the time&quot;))

sbGLMp$perceive_extreme_very.f &lt;- 
  factor(sbGLMp$perceive_extreme_very.f, levels=c(&quot;0&quot;,&quot;1&quot;),
         labels=c(&quot;Not extreme&quot;, 
                  &quot;Outparty is extreme&quot;))

sbGLMp$educ.int.f &lt;- 
  factor(sbGLMp$educ.int, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;,&quot;5&quot;),
         labels=c(&quot;H.S. or less&quot;, 
                  &quot;Some College&quot;,
                  &quot;2-Year Degree&quot;,
                  &quot;4-Year Degree&quot;,
                  &quot;Post-Graduate&quot;))

sbGLMp$Pres_Vote.f &lt;- 
  factor(sbGLMp$Pres_Vote.f, 
         levels = c(&quot;Solid Democrat&quot;, 
                    &quot;Lean Democrat&quot;,
                    &quot;Lean Republican&quot;,
                    &quot;Solid Republican&quot;),
         labels=c(&quot;Solid Democrat&quot;, 
                  &quot;Lean Democrat&quot;,
                  &quot;Lean Republican&quot;,
                  &quot;Solid Republican&quot;))


label(sbGLMp$pid.f) &lt;- &quot;Party&quot;
label(sbGLMp$RCO322_p) &lt;- &quot;Preference for Partisan School Board&quot;
label(sbGLMp$Pres_Vote.f) &lt;- &quot;County 2016 Pres. Vote&quot;
label(sbGLMp$educ.int.f) &lt;- &quot;Education&quot;
label(sbGLMp$perceive_extreme_very.f) &lt;- &quot;Outparty Extremity&quot;
label(sbGLMp$polint.f) &lt;- &quot;News Interest&quot;


###################################################

## LEO ##

leoGLMp &lt;- cces2.2

leoGLMp$pid.f &lt;- as.factor(leoGLMp$pid)
leoGLMp$polint.f &lt;- as.factor(leoGLMp$polint)
leoGLMp$perceive_extreme_very.f &lt;- as.factor(leoGLMp$perceive_extreme_very)
leoGLMp$educ.int.f &lt;- as.factor(leoGLMp$educ.int)
leoGLMp$Pres_Vote.f &lt;- as.factor(leoGLMp$Pres_Vote)

## Change Labels ##

leoGLMp$polint.f &lt;- 
  factor(leoGLMp$polint.f, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;),
         labels=c(&quot;News: Hardly at all&quot;, 
                  &quot;News: Now and then&quot;,
                  &quot;News: Some of the time&quot;,
                  &quot;News: Most of the time&quot;))

leoGLMp$perceive_extreme_very.f &lt;- 
  factor(leoGLMp$perceive_extreme_very.f, levels=c(&quot;0&quot;,&quot;1&quot;),
         labels=c(&quot;Not extreme&quot;, 
                  &quot;Outparty is extreme&quot;))

leoGLMp$educ.int.f &lt;- 
  factor(leoGLMp$educ.int, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;,&quot;5&quot;),
         labels=c(&quot;H.S. or less&quot;, 
                  &quot;Some College&quot;,
                  &quot;2-Year Degree&quot;,
                  &quot;4-Year Degree&quot;,
                  &quot;Post-Graduate&quot;))

leoGLMp$Pres_Vote.f &lt;- 
  factor(leoGLMp$Pres_Vote.f, 
         levels = c(&quot;Solid Democrat&quot;, 
                    &quot;Lean Democrat&quot;,
                    &quot;Lean Republican&quot;,
                    &quot;Solid Republican&quot;),
         labels=c(&quot;Solid Democrat&quot;, 
                  &quot;Lean Democrat&quot;,
                  &quot;Lean Republican&quot;,
                  &quot;Solid Republican&quot;))


label(leoGLMp$pid.f) &lt;- &quot;Party&quot;
label(leoGLMp$RCO321_p) &lt;- &quot;Preference for Partisan Local Election Official&quot;
label(leoGLMp$Pres_Vote.f) &lt;- &quot;County 2016 Pres. Vote&quot;
label(leoGLMp$educ.int.f) &lt;- &quot;Education&quot;
label(leoGLMp$perceive_extreme_very.f) &lt;- &quot;Outparty Extremity&quot;
label(leoGLMp$polint.f) &lt;- &quot;News Interest&quot;



###################################################

## Sheriff ##

sherGLMp &lt;- cces2.2

sherGLMp$pid.f &lt;- as.factor(sherGLMp$pid)
sherGLMp$polint.f &lt;- as.factor(sherGLMp$polint)
sherGLMp$perceive_extreme_very.f &lt;- as.factor(sherGLMp$perceive_extreme_very)
sherGLMp$educ.int.f &lt;- as.factor(sherGLMp$educ.int)
sherGLMp$Pres_Vote.f &lt;- as.factor(sherGLMp$Pres_Vote)

## Change Labels ##

sherGLMp$polint.f &lt;- 
  factor(sherGLMp$polint.f, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;),
         labels=c(&quot;News: Hardly at all&quot;, 
                  &quot;News: Now and then&quot;,
                  &quot;News: Some of the time&quot;,
                  &quot;News: Most of the time&quot;))

sherGLMp$perceive_extreme_very.f &lt;- 
  factor(sherGLMp$perceive_extreme_very.f, levels=c(&quot;0&quot;,&quot;1&quot;),
         labels=c(&quot;Not extreme&quot;, 
                  &quot;Outparty is extreme&quot;))

sherGLMp$educ.int.f &lt;- 
  factor(sherGLMp$educ.int, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;,&quot;5&quot;),
         labels=c(&quot;H.S. or less&quot;, 
                  &quot;Some College&quot;,
                  &quot;2-Year Degree&quot;,
                  &quot;4-Year Degree&quot;,
                  &quot;Post-Graduate&quot;))

sherGLMp$Pres_Vote.f &lt;- 
  factor(sherGLMp$Pres_Vote.f, 
         levels = c(&quot;Solid Democrat&quot;, 
                    &quot;Lean Democrat&quot;,
                    &quot;Lean Republican&quot;,
                    &quot;Solid Republican&quot;),
         labels=c(&quot;Solid Democrat&quot;, 
                  &quot;Lean Democrat&quot;,
                  &quot;Lean Republican&quot;,
                  &quot;Solid Republican&quot;))


label(sherGLMp$pid.f) &lt;- &quot;Party&quot;
label(sherGLMp$RCO323_p) &lt;- &quot;Preference for Partisan Sheriff&quot;
label(sherGLMp$Pres_Vote.f) &lt;- &quot;County 2016 Pres. Vote&quot;
label(sherGLMp$educ.int.f) &lt;- &quot;Education&quot;
label(sherGLMp$perceive_extreme_very.f) &lt;- &quot;Outparty Extremity&quot;
label(sherGLMp$polint.f) &lt;- &quot;News Interest&quot;


###################################################

## DA ##

daGLMp &lt;- cces2.2

daGLMp$pid.f &lt;- as.factor(daGLMp$pid)
daGLMp$polint.f &lt;- as.factor(daGLMp$polint)
daGLMp$perceive_extreme_very.f &lt;- as.factor(daGLMp$perceive_extreme_very)
daGLMp$educ.int.f &lt;- as.factor(daGLMp$educ.int)
daGLMp$Pres_Vote.f &lt;- as.factor(daGLMp$Pres_Vote)

## Change Labels ##

daGLMp$polint.f &lt;- 
  factor(daGLMp$polint.f, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;),
         labels=c(&quot;News: Hardly at all&quot;, 
                  &quot;News: Now and then&quot;,
                  &quot;News: Some of the time&quot;,
                  &quot;News: Most of the time&quot;))

daGLMp$perceive_extreme_very.f &lt;- 
  factor(daGLMp$perceive_extreme_very.f, levels=c(&quot;0&quot;,&quot;1&quot;),
         labels=c(&quot;Not extreme&quot;, 
                  &quot;Outparty is extreme&quot;))

daGLMp$educ.int.f &lt;- 
  factor(daGLMp$educ.int, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;,&quot;5&quot;),
         labels=c(&quot;H.S. or less&quot;, 
                  &quot;Some College&quot;,
                  &quot;2-Year Degree&quot;,
                  &quot;4-Year Degree&quot;,
                  &quot;Post-Graduate&quot;))

daGLMp$Pres_Vote.f &lt;- 
  factor(daGLMp$Pres_Vote.f, 
         levels = c(&quot;Solid Democrat&quot;, 
                    &quot;Lean Democrat&quot;,
                    &quot;Lean Republican&quot;,
                    &quot;Solid Republican&quot;),
         labels=c(&quot;Solid Democrat&quot;, 
                  &quot;Lean Democrat&quot;,
                  &quot;Lean Republican&quot;,
                  &quot;Solid Republican&quot;))


label(daGLMp$pid.f) &lt;- &quot;Party&quot;
label(daGLMp$RCO324_p) &lt;- &quot;Preference for Partisan District Attorney&quot;
label(daGLMp$Pres_Vote.f) &lt;- &quot;County 2016 Pres. Vote&quot;
label(daGLMp$educ.int.f) &lt;- &quot;Education&quot;
label(daGLMp$perceive_extreme_very.f) &lt;- &quot;Outparty Extremity&quot;
label(daGLMp$polint.f) &lt;- &quot;News Interest&quot;


###################################################

## U.S. House ##

houseGLMp &lt;- cces2.2

houseGLMp$pid.f &lt;- as.factor(houseGLMp$pid)
houseGLMp$polint.f &lt;- as.factor(houseGLMp$polint)
houseGLMp$perceive_extreme_very.f &lt;- as.factor(houseGLMp$perceive_extreme_very)
houseGLMp$educ.int.f &lt;- as.factor(houseGLMp$educ.int)
houseGLMp$Pres_Vote.f &lt;- as.factor(houseGLMp$Pres_Vote)

## Change Labels ##

houseGLMp$polint.f &lt;- 
  factor(houseGLMp$polint.f, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;),
         labels=c(&quot;News: Hardly at all&quot;, 
                  &quot;News: Now and then&quot;,
                  &quot;News: Some of the time&quot;,
                  &quot;News: Most of the time&quot;))

houseGLMp$perceive_extreme_very.f &lt;- 
  factor(houseGLMp$perceive_extreme_very.f, levels=c(&quot;0&quot;,&quot;1&quot;),
         labels=c(&quot;Not extreme&quot;, 
                  &quot;Outparty is extreme&quot;))

houseGLMp$educ.int.f &lt;- 
  factor(houseGLMp$educ.int, levels=c(&quot;1&quot;,&quot;2&quot;,&quot;3&quot;,&quot;4&quot;,&quot;5&quot;),
         labels=c(&quot;H.S. or less&quot;, 
                  &quot;Some College&quot;,
                  &quot;2-Year Degree&quot;,
                  &quot;4-Year Degree&quot;,
                  &quot;Post-Graduate&quot;))

houseGLMp$Pres_Vote.f &lt;- 
  factor(houseGLMp$Pres_Vote.f, 
         levels = c(&quot;Solid Democrat&quot;, 
                    &quot;Lean Democrat&quot;,
                    &quot;Lean Republican&quot;,
                    &quot;Solid Republican&quot;),
         labels=c(&quot;Solid Democrat&quot;, 
                  &quot;Lean Democrat&quot;,
                  &quot;Lean Republican&quot;,
                  &quot;Solid Republican&quot;))


label(houseGLMp$pid.f) &lt;- &quot;Party&quot;
label(houseGLMp$RCO327_p) &lt;- &quot;Preference for Partisan U.S. House&quot;
label(houseGLMp$Pres_Vote.f) &lt;- &quot;County 2016 Pres. Vote&quot;
label(houseGLMp$educ.int.f) &lt;- &quot;Education&quot;
label(houseGLMp$perceive_extreme_very.f) &lt;- &quot;Outparty Extremity&quot;
label(houseGLMp$polint.f) &lt;- &quot;News Interest&quot;


########################################################################
########################################################################
########################################################################
########################################################################

# Replication code for Tables and Figures in main paper #

########################################################################
########################################################################
########################################################################
########################################################################

# Code below assumes that 'r_script' has been run and resulting datasets are loaded in environment #

library(&quot;readxl&quot;)
library(sm)
library(pastecs)
library(psych)
library(dplyr)
library(&quot;ggpubr&quot;)
library(&quot;gplots&quot;)
library(rstatix)
library(ggplot2)
library(jtools)
library(stargazer)
library(devtools)
library(sjmisc)
library(sjPlot)
library(table1)
library(boot)
library(expss)
library(summarytools)
library(tidyverse)</code></pre>
<pre><code>## ── Attaching packages ─────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0.9000 ──</code></pre>
<pre><code>## ✓ tibble  3.0.1     ✓ purrr   0.3.3
## ✓ readr   1.3.1     ✓ forcats 0.5.0</code></pre>
<pre><code>## ── Conflicts ─────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x ggplot2::%+%()         masks psych::%+%()
## x tibble::add_case()     masks sjmisc::add_case()
## x ggplot2::alpha()       masks psych::alpha()
## x forcats::as_factor()   masks sjlabelled::as_factor()
## x sjlabelled::as_label() masks dplyr::as_label()
## x expss::between()       masks dplyr::between()
## x expss::compute()       masks dplyr::compute()
## x tidyr::contains()      masks expss::contains(), dplyr::contains()
## x tidyr::extract()       masks pastecs::extract()
## x rstatix::filter()      masks dplyr::filter(), stats::filter()
## x expss::first()         masks pastecs::first(), dplyr::first()
## x stringr::fixed()       masks expss::fixed()
## x purrr::is_empty()      masks sjmisc::is_empty()
## x purrr::keep()          masks expss::keep()
## x dplyr::lag()           masks stats::lag()
## x expss::last()          masks pastecs::last(), dplyr::last()
## x purrr::modify()        masks expss::modify()
## x purrr::modify_if()     masks expss::modify_if()
## x expss::na_if()         masks dplyr::na_if()
## x tidyr::nest()          masks expss::nest()
## x expss::recode()        masks dplyr::recode()
## x stringr::regex()       masks expss::regex()
## x tidyr::replace_na()    masks sjmisc::replace_na()
## x Hmisc::src()           masks dplyr::src()
## x Hmisc::summarize()     masks dplyr::summarize()
## x purrr::transpose()     masks expss::transpose()
## x expss::vars()          masks ggplot2::vars(), dplyr::vars()
## x tibble::view()         masks summarytools::view()
## x purrr::when()          masks expss::when()</code></pre>
<pre class="r"><code>library(expss)

##################################################################

# Table 1: Descriptive Statistics, CES Module, 2018+2020 (pooled) #

##################################################################


#re-name data for organization purposes
desc.stats &lt;-cces2.2

desc.stats2 &lt;- desc.stats %&gt;% select(pid, educ.int,
                                     perceive_extreme_very,
                                     polint, Pres_Vote, 
                                     cces.year)

desc.stats2 = apply_labels(desc.stats2,
                           pid = &quot;Party (inc. leaners)&quot;,
                           educ.int = &quot;Education (highest degree)&quot;,
                           perceive_extreme_very = &quot;Out-party is extreme&quot;,
                           polint = &quot;News Interest&quot;,
                           Pres_Vote = &quot;2016 County Pres. Vote&quot;,
                           cces.year = &quot;CCES Year&quot;)

dfs &lt;- (dfSummary(desc.stats2, plain.ascii = FALSE, style = &quot;grid&quot;, 
                  graph.magnif = 0.75, missing.col = FALSE))

dfs</code></pre>
<pre><code>## text graphs are displayed; set 'tmp.img.dir' parameter to activate png graphs</code></pre>
<pre><code>## ### Data Frame Summary  
## #### desc.stats2  
## **Dimensions:** 1657 x 6  
## **Duplicates:** 1205  
## 
## +----+------------------------+----------------------------+------------------------+---------------------+----------------+----------+---------+
## | No | Variable               | Label                      | Stats / Values         | Freqs (% of Valid)  | Graph          | Valid    | Missing |
## +====+========================+============================+========================+=====================+================+==========+=========+
## | 1  | pid\                   | Party (inc. leaners)       | 1\. Democrat\          | 945 (57.0%)\        | IIIIIIIIIII \  | 1657\    | 0\      |
## |    | [labelled, factor]     |                            | 2\. Republican         | 712 (43.0%)         | IIIIIIII       | (100.0%) | (0.0%)  |
## +----+------------------------+----------------------------+------------------------+---------------------+----------------+----------+---------+
## | 2  | educ.int\              | Education (highest degree) | Mean (sd) : 2.7 (1.5)\ | 1 : 490 (29.6%)\    | IIIII \        | 1657\    | 0\      |
## |    | [labelled, integer]    |                            | min &lt; med &lt; max:\      | 2 : 348 (21.0%)\    | IIII \         | (100.0%) | (0.0%)  |
## |    |                        |                            | 1 &lt; 2 &lt; 5\             | 3 : 170 (10.3%)\    | II \           |          |         |
## |    |                        |                            | IQR (CV) : 3 (0.5)     | 4 : 387 (23.4%)\    | IIII \         |          |         |
## |    |                        |                            |                        | 5 : 262 (15.8%)     | III            |          |         |
## +----+------------------------+----------------------------+------------------------+---------------------+----------------+----------+---------+
## | 3  | perceive_extreme_very\ | Out-party is extreme       | Min  : 0\              | 0 : 918 (55.4%)\    | IIIIIIIIIII \  | 1657\    | 0\      |
## |    | [labelled, integer]    |                            | Mean : 0.4\            | 1 : 739 (44.6%)     | IIIIIIII       | (100.0%) | (0.0%)  |
## |    |                        |                            | Max  : 1               |                     |                |          |         |
## +----+------------------------+----------------------------+------------------------+---------------------+----------------+----------+---------+
## | 4  | polint\                | News Interest              | Mean (sd) : 3.4 (0.9)\ | 1 :  71 ( 4.4%)\    | \              | 1631\    | 26\     |
## |    | [labelled, integer]    |                            | min &lt; med &lt; max:\      | 2 : 187 (11.5%)\    | II \           | (98.4%)  | (1.6%)  |
## |    |                        |                            | 1 &lt; 4 &lt; 4\             | 3 : 448 (27.5%)\    | IIIII \        |          |         |
## |    |                        |                            | IQR (CV) : 1 (0.3)     | 4 : 925 (56.7%)     | IIIIIIIIIII    |          |         |
## +----+------------------------+----------------------------+------------------------+---------------------+----------------+----------+---------+
## | 5  | Pres_Vote\             | 2016 County Pres. Vote     | 1\. Lean Democrat\     | 518 (31.3%)\        | IIIIII \       | 1657\    | 0\      |
## |    | [labelled, character]  |                            | 2\. Lean Republican\   | 452 (27.3%)\        | IIIII \        | (100.0%) | (0.0%)  |
## |    |                        |                            | 3\. Solid Democrat\    | 348 (21.0%)\        | IIII \         |          |         |
## |    |                        |                            | 4\. Solid Republican   | 339 (20.5%)         | IIII           |          |         |
## +----+------------------------+----------------------------+------------------------+---------------------+----------------+----------+---------+
## | 6  | cces.year\             | CCES Year                  | Min  : 2018\           | 2018 : 844 (50.9%)\ | IIIIIIIIII \   | 1657\    | 0\      |
## |    | [labelled, integer]    |                            | Mean : 2019\           | 2020 : 813 (49.1%)  | IIIIIIIII      | (100.0%) | (0.0%)  |
## |    |                        |                            | Max  : 2020            |                     |                |          |         |
## +----+------------------------+----------------------------+------------------------+---------------------+----------------+----------+---------+</code></pre>
<pre class="r"><code>##################################################################
# summarySE function (for barplots) #
##################################################################
summarySE &lt;- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
                      conf.interval=.95, .drop=TRUE) {
  library(plyr)
  
  # New version of length which can handle NA's: if na.rm==T, don't count them
  length2 &lt;- function (x, na.rm=FALSE) {
    if (na.rm) sum(!is.na(x))
    else       length(x)
  }
  
  # This does the summary. For each group's data frame, return a vector with
  # N, mean, and sd
  datac &lt;- ddply(data, groupvars, .drop=.drop,
                 .fun = function(xx, col) {
                   c(N    = length2(xx[[col]], na.rm=na.rm),
                     mean = mean   (xx[[col]], na.rm=na.rm),
                     sd   = sd     (xx[[col]], na.rm=na.rm)
                   )
                 },
                 measurevar
  )
  
  # Rename the &quot;mean&quot; column    
  datac &lt;- rename(datac, c(&quot;mean&quot; = measurevar))
  
  datac$se &lt;- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean
  
  # Confidence interval multiplier for standard error
  # Calculate t-statistic for confidence interval: 
  # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
  ciMult &lt;- qt(conf.interval/2 + .5, datac$N-1)
  datac$ci &lt;- datac$se * ciMult
  
  return(datac)
}

##################################################################

# Figure 1. Proportion of Democrats and Republicans choosing &quot;nonpartisan election&quot; #

##################################################################
#sb

sbPlot &lt;- cces2.2
sbPlot$pid &lt;- factor(sbPlot$pid)
sbPlot &lt;- summarySE(cces2.2, measurevar=&quot;RCO322_np&quot;, groupvars=c(&quot;pid&quot;), na.rm=TRUE)</code></pre>
<pre><code>## ------------------------------------------------------------------------------</code></pre>
<pre><code>## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)</code></pre>
<pre><code>## ------------------------------------------------------------------------------</code></pre>
<pre><code>## 
## Attaching package: 'plyr'</code></pre>
<pre><code>## The following object is masked from 'package:purrr':
## 
##     compact</code></pre>
<pre><code>## The following objects are masked from 'package:Hmisc':
## 
##     is.discrete, summarize</code></pre>
<pre><code>## The following object is masked from 'package:expss':
## 
##     split_labels</code></pre>
<pre><code>## The following objects are masked from 'package:rstatix':
## 
##     desc, mutate</code></pre>
<pre><code>## The following object is masked from 'package:ggpubr':
## 
##     mutate</code></pre>
<pre><code>## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize</code></pre>
<pre class="r"><code>sbPlot$office &lt;- &quot;school board&quot;
sbPlot$proportion &lt;- sbPlot$RCO322_np
sbPlot$RCO322_np &lt;- NULL

sb_party_Plot &lt;- ggplot(sbPlot, aes(x=pid, y=proportion, fill=pid)) 
sb_party_Plot + 
  scale_fill_manual(values=c(&quot;#00AFBB&quot;, &quot;#FC4E07&quot;),
                    name=&quot;Party&quot;, # Legend label, use darker colors
                    breaks=c(&quot;Democrat&quot;, &quot;Republican&quot;),
                    labels=c(&quot;Democrat&quot;, &quot;Republican&quot;)) +
  geom_bar(position=position_dodge(), stat=&quot;identity&quot;,
           colour=&quot;black&quot;, # Use black outlines,
           size=.3) +      # Thinner lines
  geom_errorbar(aes(ymin=proportion-se, ymax=proportion+se),
                size=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9))+
  xlab(&quot;&quot;) +
  scale_x_discrete(breaks=c(&quot;Democrat&quot;,&quot;Republican&quot;),
                   labels=c(&quot;&quot;, &quot;&quot;)) +
  ylab(&quot;proportion&quot;) +
  ylim(0,0.8) +
  ggtitle(&quot;&quot;) +
  theme_bw()</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<pre class="r"><code>#leo

leoPlot &lt;- cces2.2
leoPlot$pid &lt;- factor(leoPlot$pid)
leoPlot &lt;- summarySE(cces2.2, measurevar=&quot;RCO321_np&quot;, groupvars=c(&quot;pid&quot;), na.rm=TRUE)
leoPlot$office &lt;- &quot;local election official&quot;
leoPlot$proportion &lt;- leoPlot$RCO321_np
leoPlot$RCO321_np &lt;- NULL

leo_party_Plot &lt;- ggplot(leoPlot, aes(x=pid, y=proportion, fill=pid)) 
leo_party_Plot + 
  scale_fill_manual(values=c(&quot;#00AFBB&quot;, &quot;#FC4E07&quot;),
                    name=&quot;Party&quot;, # Legend label, use darker colors
                    breaks=c(&quot;Democrat&quot;, &quot;Republican&quot;),
                    labels=c(&quot;Democrat&quot;, &quot;Republican&quot;)) +
  geom_bar(position=position_dodge(), stat=&quot;identity&quot;,
           colour=&quot;black&quot;, # Use black outlines,
           size=.3) +      # Thinner lines
  geom_errorbar(aes(ymin=proportion-se, ymax=proportion+se),
                size=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9))+
  xlab(&quot;&quot;) +
  scale_x_discrete(breaks=c(&quot;Democrat&quot;,&quot;Republican&quot;),
                   labels=c(&quot;&quot;, &quot;&quot;)) +
  ylab(&quot;proportion&quot;) +
  ylim(0,0.8) +
  ggtitle(&quot;&quot;) +
  theme_bw()</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>#sheriff

sherPlot &lt;- cces2.2
sherPlot$pid &lt;- factor(sherPlot$pid)
sherPlot &lt;- summarySE(cces2.2, measurevar=&quot;RCO323_np&quot;, groupvars=c(&quot;pid&quot;), na.rm=TRUE)
sherPlot$office &lt;- &quot;sheriff&quot;
sherPlot$proportion &lt;- sherPlot$RCO323_np
sherPlot$RCO323_np &lt;- NULL

sher_party_Plot &lt;- ggplot(sherPlot, aes(x=pid, y=proportion, fill=pid)) 
sher_party_Plot + 
  scale_fill_manual(values=c(&quot;#00AFBB&quot;, &quot;#FC4E07&quot;),
                    name=&quot;Party&quot;, # Legend label, use darker colors
                    breaks=c(&quot;Democrat&quot;, &quot;Republican&quot;),
                    labels=c(&quot;Democrat&quot;, &quot;Republican&quot;)) +
  geom_bar(position=position_dodge(), stat=&quot;identity&quot;,
           colour=&quot;black&quot;, # Use black outlines,
           size=.3) +      # Thinner lines
  geom_errorbar(aes(ymin=proportion-se, ymax=proportion+se),
                size=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9))+
  xlab(&quot;&quot;) +
  scale_x_discrete(breaks=c(&quot;Democrat&quot;,&quot;Republican&quot;),
                   labels=c(&quot;&quot;, &quot;&quot;)) +
  ylab(&quot;proportion&quot;) +
  ylim(0,0.8) +
  ggtitle(&quot;&quot;) +
  theme_bw()</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>#da

daPlot &lt;- cces2.2
daPlot$pid &lt;- factor(daPlot$pid)
daPlot &lt;- summarySE(cces2.2, measurevar=&quot;RCO324_np&quot;, groupvars=c(&quot;pid&quot;), na.rm=TRUE)
daPlot$office &lt;- &quot;district attorney&quot;
daPlot$proportion &lt;- daPlot$RCO324_np
daPlot$RCO324_np &lt;- NULL

da_party_Plot &lt;- ggplot(daPlot, aes(x=pid, y=proportion, fill=pid)) 
da_party_Plot + 
  scale_fill_manual(values=c(&quot;#00AFBB&quot;, &quot;#FC4E07&quot;),
                    name=&quot;Party&quot;, # Legend label, use darker colors
                    breaks=c(&quot;Democrat&quot;, &quot;Republican&quot;),
                    labels=c(&quot;Democrat&quot;, &quot;Republican&quot;)) +
  geom_bar(position=position_dodge(), stat=&quot;identity&quot;,
           colour=&quot;black&quot;, # Use black outlines,
           size=.3) +      # Thinner lines
  geom_errorbar(aes(ymin=proportion-se, ymax=proportion+se),
                size=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9))+
  xlab(&quot;&quot;) +
  scale_x_discrete(breaks=c(&quot;Democrat&quot;,&quot;Republican&quot;),
                   labels=c(&quot;&quot;, &quot;&quot;)) +
  ylab(&quot;proportion&quot;) +
  ylim(0,0.8) +
  ggtitle(&quot;&quot;) +
  theme_bw()</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>#House

housePlot &lt;- cces2.2
housePlot$pid &lt;- factor(housePlot$pid)
housePlot &lt;- summarySE(cces2.2, measurevar=&quot;RCO327_np&quot;, groupvars=c(&quot;pid&quot;), na.rm=TRUE)
housePlot$office &lt;- &quot;u.s. house&quot;
housePlot$proportion &lt;- housePlot$RCO327_np
housePlot$RCO327_np &lt;- NULL

house_party_Plot &lt;- ggplot(housePlot, aes(x=pid, y=proportion, fill=pid)) 
house_party_Plot + 
  scale_fill_manual(values=c(&quot;#00AFBB&quot;, &quot;#FC4E07&quot;),
                    name=&quot;Party&quot;, # Legend label, use darker colors
                    breaks=c(&quot;Democrat&quot;, &quot;Republican&quot;),
                    labels=c(&quot;Democrat&quot;, &quot;Republican&quot;)) +
  geom_bar(position=position_dodge(), stat=&quot;identity&quot;,
           colour=&quot;black&quot;, # Use black outlines,
           size=.3) +      # Thinner lines
  geom_errorbar(aes(ymin=proportion-se, ymax=proportion+se),
                size=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9))+
  xlab(&quot;&quot;) +
  scale_x_discrete(breaks=c(&quot;Democrat&quot;,&quot;Republican&quot;),
                   labels=c(&quot;&quot;, &quot;&quot;)) +
  ylab(&quot;proportion&quot;) +
  ylim(0,0.8) +
  ggtitle(&quot;&quot;) +
  theme_bw()</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>### Combining dataframes ##

localofficePlot &lt;- rbind(sbPlot, leoPlot, sherPlot, daPlot, housePlot)

localofficePlot$office &lt;- ordered(localofficePlot$office, 
                                  levels = c(&quot;school board&quot;, &quot;sheriff&quot;,
                                             &quot;district attorney&quot;, 
                                             &quot;local election official&quot;,
                                             &quot;u.s. house&quot;))

local_np_partyplot &lt;- ggplot(localofficePlot, aes(x=office, y=proportion, fill=pid)) + 
  xlab(&quot;Office&quot;) +
  ylab(&quot;Proportion&quot;) +
  scale_fill_manual(values=c(&quot;blue&quot;, &quot;red&quot;),
                    name=&quot;Party&quot;, # Legend label, use darker colors
                    breaks=c(&quot;Democrat&quot;, &quot;Republican&quot;),
                    labels=c(&quot;Democrat&quot;, &quot;Republican&quot;)) +
  geom_bar(position=position_dodge(), stat=&quot;identity&quot;,
           colour=&quot;black&quot;, # Use black outlines,
           size=.3) +      # Thinner lines
  geom_errorbar(aes(ymin=proportion-se, ymax=proportion+se),
                size=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9))+
  ggtitle(&quot;Preference for Nonpartisan elections&quot;) +
  ylim(0,1)+
  theme_bw()

local_np_partyplot</code></pre>
<p><img 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AACCCCAQOEECIAWrsjJMAIIIIAAAggggAACCCCAAAIIIIAAAsURIABanLImpwgggAACCCCAAAIIIIAAAggggAACCBROgABo4YqcDCOAAAIIIIAAAggggAACCCCAAAIIIFAcAQKgxSlrcooAAggggAACCCCAAAIIIIAAAggggEDhBAiAFq7IyTACCCCAAAIIIIAAAggggAACCCCAAALFESAAWpyyJqcIIIAAAggggAACCCCAAAIIIIAAAggUToAAaOGKnAwjgAACCCCAAAIIIIAAAggggAACCCBQHAECoMUpa3KKAAIIIIAAAggggAACCCCAAAIIIIBA4QQIgBauyMkwAggggAACCCCAAAIIIIAAAggggAACxREgAFqcsianCCCAAAIIIIAAAggggAACCCCAAAIIFE6AAGjhipwMI4AAAggggAACCCCAAAIIIIAAAgggUBwBAqDFKWtyigACCCCAAAIIIIAAAggggAACCCCAQOEECIAWrsjJMAIIIIAAAggggAACCCCAAAIIIIAAAsURIABanLImpwgggAACCCCAAAIIIIAAAggggAACCBROgABo4YqcDCOAAAIIIIAAAggggAACCCCAAAIIIFAcAQKgxSlrcooAAggggAACCCCAAAIIIIAAAggggEDhBAiAFq7IyTACCCCAAAIIIIAAAggggAACCCCAAALFESAAWpyyJqcIIIAAAggggAACCCCAAAIIIIAAAggUToAAaOGKnAwjgAACCCCAAAIIIIAAAggggAACCCBQHAECoMUpa3KKAAIIIIAAAggggAACCCCAAAIIIIBA4QQIgBauyMkwAggggAACCCCAAAIIIIAAAggggAACxRHoVJys5ienM2fOtCVLluQnQwXKyfvvv2+rVq1yOe7Tp0+Bck5WiyqwfPly059St27drEuXLkWlIN8FEdA5Xud6pY4dO1rPnj0LknOyWWSBZcuW2ccff+wIevToYZ06cYtR5OOhCHlfuXKlffDBBy6rurbRNQ6p/QnMnTu3/e00e4wAApkFOjQEKfPSLNiqAosWLbLhw4e36jbZGAIIIIAAAggggAACCCCAAAJ5FNhss83svvvuy2PWyBMCCMQECIDGQOr97YoVK+p9F9m/JgS++c1v2sKFC61z5872+OOPNzEnkxDIh8CECRPsiiuucJk599xzbcSIEfnIGLlAoIKAasL5HyuHDBliEydOrDAnoxHIj8BZZ51lU6ZMcRm65ZZbbMstt8xP5sgJAmUEnn76aTv66KPdlAMPPNBGjx5dZi5GtQeBDh06UGu9PRQU+4hADQRon1IDxNZchQJnpPYroAC2mswoUZbttxzZ83QC/pjXUhz36eyYu/0JqOmvP+Y/+eQTjvn2V4TscQYBNSjzx/1qq63GcZ/BkEXal4COc3/M6/jn+qZ9lR97iwACxRSgE6Rilju5RgABBBBAAAEEEEAAAQQQQAABBBBAoBACBEALUcxkEgEEEEAAAQQQQAABBBBAAAEEEEAAgWIKEAAtZrmTawQQQAABBBBAAAEEEEAAAQQQQAABBAohQAC0EMVMJhFAAAEEEEAAAQQQQAABBBBAAAEEECimAAHQYpY7uUYAAQQQQAABBBBAAAEEEEAAAQQQQKAQAh2CXusaCpFTMolAHQjMmDHDli1bZuo5cu+9966DPWIXEGhZgfnz59sLL7zgNrLVVltZ//79W3aDrB2BNhZQz+8PPvig24vevXvbV7/61TbeIzaPQMsLPPvss/baa6+5DemY17FPQiDPAosXL7ZHH33UZVHXNrrGISGAAAII1LcAAdD6Lh/2DgEEEEAAAQQQQAABBBBAAAEEEEAAAQSqEKAJfBV4LIoAAggggAACCCCAAAIIIIAAAggggAAC9S1AALS+y4e9QwABBBBAAAEEEEAAAQQQQAABBBBAAIEqBAiAVoHHoggggAACCCCAAAIIIIAAAggggAACCCBQ3wIEQOu7fNg7BBBAAIFmBFauXGkrVqwoO9eHH35YdjwjEUAAAQQQSCPAd00aLeZFAAEEEECg/gQIgNZfmbBHGQV+9KMf2bHHHmvqlbHekvZLf+oBPkk64YQT3PzvvfdektnbzTyvvPKKy9fPfvazdrPPRd7RevlMnXzyye64WbRoUUlxPPXUU6Zp++yzj+2xxx72n//5n266blLHjBljhx56qO2999625557mnooJtVeoNJnulKZpd0D9ajue5ZOuqx6YNf59sYbb0y6SMX5Fi5cWHFaLSdU2k6W/Ndyv+pxXffcc48r31tvvbUed69kn1pjX1tjGyWZir0pd+zW6vMf21SbvU37XVOL/NdiHQJLe/3ZZsh1uuE//vGPznDChAl1uofsFgIIIIBAGoFOaWZmXgTqWeCll14y1fZS8KPe0gsvvOB2STezSdKLL75oH3/8ccVabUnWUY/zqHxksXTp0nrcPfYpJlAvnyl9HvTjQfSz/dZbb9nZZ59tS5YssY4dO9rGG29sAwYMcDm49tpr7a677nLDa6+9tnXu3Nn69esXyx1vayFQ6TNdrszSbm/OnDl28cUXuwD2d77zncSLv/POO+48s9lmmyVeJj6jztXXXXed/c///I898MAD8ck1e9/UdrLmv2Y7V6cr+te//uXKd+utt67TPfx8t1pjX1tjG5/n6POhpo7dWnz+P99S2w5l+a6pRf5rsQ7Jpb3+bFvt+tu6KlXIcNCgQfW3c+wRAggggEBqAQKgqclYAAEEEECgNQUOOOAA94NAt27dws3+/e9/d8HPtdZay/77v//bevbsGU6bOXOmG/7hD39ohx9+uHXo0CGcxkDrCJQrs7Rbnjp1qs2bN88FQNMs+8UvftEOPvhg23LLLdMsVjLvBx98YBMnTrROnVr2Mqmp7WTNf0lGeINACwk0dezW4vPfQruderVZvmtqkf9arCN1ZlkAAQQQQACBnAu07JV9zvHIHgIIIIBAywuceOKJjTby9ttvu3HbbbddSfBTI/20nXfemeBnI7nWGVGuzFpny2Y6JvRHQgCBthFoy89/rXPsv0/SfNfUIv+1WEetLVgfAggggAAC7V2AAGh7L8F2uP9qnjh79mxTs63111/fBg4caH379m0yJ6tWrTI1x1UzFDVp3WKLLUw1vyolNc2aP3++qQnROuusY0OGDLHu3btXmt01rVVNIy2j5rRf+MIXbKONNmqy9o+a46ZdpuIONDHho48+cvnWs/Y22WQT1wynS5cuFZfIsl/ahp7jpT/V6lBzYTUpll08eSNNf/311+2ZZ56xbbbZxjbccMP4rM5TZS3PapqjNloxI6oSyPIZTPuZ0mdWx9PcuXNNy6r5mI4Zfb7iqbljasGCBab16TOpR0O8+eabYZCzoaHBHWeq5anPhY5l31Re+dS2da7o3bt3fLO8zyCgskrymY6WWbwWZXPHn8pM57v333/f7aHm13ZVhirLd9991z3rWeenrl272pNPPmk6Dr785S+7Y0DPTtYyqhVc7hzW3PeJvpv8c0f98aUd0SMWVlst2aPTtQ4ZvPrqq9arVy/r37+/W16PY/Cp0nY07z//+c+K+ffL61Xr0GdMzXR13tZ5tk+fPtFZ3HBSM3nJTY8pUTnrs6Z16i+67/EN6FEU2g995vVdoM97/DOnfdV8PXr0sHXXXTe+Cvdez/lVuSsPa665Ztl5mhuZ5TtQ69Rx87e//c2WL1/uzjWqQVyp9niS8m1uP5ubnsS0uXVoetb1NOVR6dj1n5GmPv9pysd/lrMel5V8kuyDHruS9bumqfxrn5qy9fvc1DrSXLP59dXLq5qU63yk86Ku5+NJ5rLXOULniiRJ5+nnn3/enX80v+4rdCyuscYaSRZPNY/2XY8nUT50L6LtVDpPaMVJz9HVuGTJf9bzQiosZkYAAQTqUIAAaB0WSp536Ve/+pXdeeedYYDC53X77be3M888s+zN6pVXXmn33XefC8z5+RXoOOyww+y4445rdOGhG051hqKAqU+6ODnmmGPcnx/nX//617/a+PHjwwsnP17BmrPOOqtsM8osy/j1pnlVgPWyyy5zF+F+Od1UnnPOOSazeMqyX3/4wx9MxvEOmmR24IEHmmohrL766uGmvve977kgxKmnnmo///nPXWBKE9Vhzre+9S03n56Zp+cw6kLWJ12Q6mH8pLYVyPIZTPuZUrDqvPPOcz8QRHOrAL6OXQXEo6m5Y+oHP/iBOz7vuOMOFwj7j//4j3DxadOmmf50TtB6dRPkkzqRUFJT+COOOMKP5jWDQNrPdLTM9EOXT0mOPwXAjjrqKL+Iqdz1p3P+SSedZPfee687v/zkJz8xNRP3Za6gmb5f7r//ftN29ttvP/e9Eq4oGEjyfaLOLu6++263mIKxfl/UGUb0MQzR9fphBSIvueQSe+yxx/yo8FWBQX3Pbbvttm5cpe38/ve/D7epGeP51zjdvOoZpZMnTw7PwRqvAK3Ow9///vfdZ0LjlJozk+NVV11lo0aNcgFADeum2if9+HD++ec3+uzK5+abb7bf/e537scGP7/2Q27f/e53wx8Sn3jiCbvwwgvdDyF6vEC5pA7yVJ6XX3552e+4cstEx2X5DlTg9oILLmh0vtKjFFRe0fNVmvKN7lea4TSmTa0363qSeFQ6dv1npNLnP235ZD0um3JJug/qOC/rd02l/Cex9fteaR1pr9n8+urlVc/nVgd1hxxyiOk6Mp7UgaEeY6Pr73333Tc+udF7mY4ePdr9aBadqODnj3/8Y/c9EB2fdVjnw0mTJtlvfvObknOdzuvjxo1rVJEj7Tk6q0va/Gc9L2R1YzkEEECg3gQIgNZbieR4f9Rjq27sdLGg5/Lpplg1NBW80M2iLjTjvfYqiHbbbbe5miAKYugm7PHHHzf18qubLtVIUWAzmnRTrBvhkSNH2gYbbGAPPfSQPffcc3bDDTe4wJ2eq+STanv4C1z1Ij18+HB30zdjxgz785//bOqNXTdiX/nKV/wiroZI2mXChVMOKJC73nrruYtE/Rqui0IFg3WT+stf/tJ23HHHcI1Z8iJvuSio+u///u+uJqd+3dZ2VLNTHYCodu63v/3tcDsaUMcnuslX0Ek1fVQzd4cddnDzzJo1y+2balmpDL70pS+5WlAqewXFSG0nkOUzqL1N85lSrTEFqVRTc9ddd3V/CqY/+uijLjClm7pf//rXNnjw4BKIpo6p6IwKpJ9yyimu1t/DDz9s6ghl9913dzVLdRyr5p+CN7rIV8BdASvVUCZlF6jVZzrp8acyUxlPnz7dFIT46le/av/2b/9mCkhFk84pqimpwLpqROmY0jmpUkr6faLjSedbBRgVyNPxrNTUujVdN7xHH320e1WQUz9SqVamepDWOVW1StVxl4K0+lGp0nZ0HDeVf92Iaz1ar2pQ6Tyr78Z//OMfdvvtt9stt9zigoj6YS9eM6k5M+2bfsDYc889XaBWzX8V1FLNTn0f6fs4WotbP2jo+1LfuQq86vOpeRW0VVBU61JwUWm33XYzBTdUu1ffGfFzgMYr+KnaftHvXLdwgn9ZvgPfeOMN932qmqcqD32n6sdABdG1L/quvf76651z2vJNsMtlZ0ljWnYFn43Msp6kHpWO3aY+I1nKx+cv7XHpl4u/ptmHWn/XJLUtVyvS5yPrNZtfPm+v+qzqM6rPr65hhw0b5q49dE+h+wRdJ6tGux6HU23S+nRd47+PdJ7705/+5M7rCuTqRwG1SFCq5hydZj+z5D/LeSHNPjEvAgggUPcCwUmahECrCAQ1Uhp22mmnhuCmrWR7QfOQhq997WtuWhAQDacFgVE3LrioadA80RTURnLTtFxwAeAm7bXXXm7cQQcd1BDctEVnbwh+sXXTguBLOD5o4tcQ/ALtxge/6obj/UBw0+OmBTd1DcHNtRudZRktqHzrL6jZ5Fff5GsQjHXzH3rooQ1BQLJk3qDDFzct6BW5IQjyuGlZ9ito5tew//77u3XJOp6CwK+bFtTgKZkU3MS68bILbgjdNK1LSfsRBJjd9OBi0Y3z/4KaM+H2ZEpqfYG0n8G0n6kg6N2gz5iO9SCw3iiD+pxpWvBjRsm0po4pzbj33nu75YIbyHC5oAaZGxfUkA7H+YEgeOOmBTdFfhSvGQWyfqbLlVna4y8IlrlyvOmmm0r2Xu/9OTUIyIXT/HkoCLS66RdddFE4Le33ic672oaOzaTJH99BzceGoIltyWI6FoObcLfO//u//wunNbWdSvkPgpxuPfp+CAL+4bo0oPPsiBEj3PSgplg4rTkzv+/K8z333BMup4GgBniDPxcEtefCaY888ojbjr6j45815ct/vwY/foTLBAEJt0xQYyoc5weCALWbFvxA4keVffXfzWPHjg2nZ/kO1MJHHnmk26bWGU0qv6DWeMk0b5SmfMvta3Q78eEspuW2kWU92pc0Hk0du/HPf9by8eZpjsu4qX+fdR+yfNfE85/WVvPH15H1ms2fK5Nef3qvlnotd7xGt3XGGWe4z11Q+zc6uuxw8COomzf4karR9KC2v5v205/+tNG0NCOix2Dwo07JokGz9YZ99tnHbUefOZ+ynKOzuKTNf9bzgs8XrwgggEAeBJI9zKruw7jsYHsQUI0sJT3XKJr0a7dqIQY3Xa42oZ+mZkpKqi0a/0U8CHxacJPnevrVMyujSTVw4s99U000Jf0C75NqpOm9mrqrBk08qfmeapCqhpFqmillWSa+3jTv1TRYtYGiSfsqD9WsUe0opSz7FVxMm9avGnmqzRFPu+yyixulWi/lUnCjFD6fyTeRV60e/RKv58WpjKJJNaqCG/boKIZbWSDtZ9DvXtLPlGrX+Bpc+vzEk2poqIaQetVVrcJ4KndMxefhfesK1PIznfX4q5RjnQfVjNInfx7y76OvWb9PoutoblhNpfVYluAGvqSWpJbTs0u32mort4pK59Tm1u+nq9m7kmo4x5+TqfOszutKqnEbT82ZqYVG8MNYyWIa55uB67nPPgU/xrlB/wgLP16v+t4KgqZulGqd+qTHEij97//+b0kz0uCC2tU01TQ/j4aTpizfgXpEi2rNyjD+iAzVclWtrm984xvu+a3aj9Yo3yym5YyyrCetR7ntVhqXpXyi60pzXEaXiw5Xuw/RdaUdroVttddsafe5Pczvv1OCYLx7rmp0n3Ve0uegVi2PdF+hR3pEk2q+q5WTUvSRT9Wco6Prb244bf6znBea2wemI4AAAu1NgCbw7a3E2vH+KqCmZ9WoSbmacavJtP4233xz13wvnjX/DE//vLTodDXrU7PcckkPJI8n32GSmov4pBsfJTW1izbpcyODf+q4Q71+6obPB22zLOPXl+VVzT7jSQEkdc6gJvraL/lk2S/doOoZn9GkG3M1Q3z55Zddk01N8xdY0fk0rMBxPKl8lSo1OVaTUDVDJbWNQNrPoN/LtJ8p/XDgg/N+Hf5VzZUVVNOxq87JoqncMRWdznDrC9TyM531+KuUazX7jjfxrjRv1u+TSusrN17fJdGm2zp36vtD51QF/H3HSpXOqeXWGR+nzlvU9FKp3HOgo+P14522Ff1+a86s0nQ9skaPklmxYoXbtv757x09JkA/fsST/+HSf39qur4btA3lQc8EVZNVpaefftoFEPTdluU84Pclzfe5PybUkUm54Lm+f6Pfwa1Rvj4faUwdYOxflvWk9Yhtssm3fn/SlE90hWmOy+hy0eFq9yG6rrTDtbCt9pot7T63h/mHDh3qOjhV2epRTWqernOKzo0KTpa7dsmar0rrit9fVHuOTrN/afPvPwPVnl/S7CPzIoAAAvUmQAC03kokx/ujWmQKQKo2iJ4TqD89z0gXKaqhGa1lpgsI1XBU0jMw06Rohxt+OX+TrFomPvmbMgVrKiXVOlDSjaRSlmXcghn+KdDpL6zii/s8VrtfujkOmmOaHqqvDpf0/ESfojfNflz01dtEx/lgSaUyU+0kUtsJpPkMRvfSH2/RceU+Uz4wo0CJnmHYVFLHSvFU7piKz8P71hWo5Wc66/FXKcdJj5dqvk8qbbvSeLUq0HMy1TO9Ap/atk/NnVP9fE29Koiq87Y6+Ii3DvDL6TyrbWnbqpUUdYoO+/mjrz5oGR2nYf0gGE36sUw9WStdeuml0UmNhtVxkJ6d558P+fWvf939EKaOtXwAVM/cVNK0LCnLd7MPSpU7v1Xah5Ys32pMo/ubdT1ZPKLbbWo4S/lE15f0uIwuEx+udh/i60vzvla21Vyzpdnf9jJv9+7d7eqrr3adlencqHOK/nR9ohr3+pHf10SvNk+VzhPxa6Fqz9Fp9jNN/rOeF9LsD/MigAAC7UGg9Iq2Pewx+9huBXSRoJ7C1QxaQTc9pFydOKjpipqLqEbjFVdcYZtuuqnLozrRUdIFX5qkXzaTJH9DF71BjS+njjWU/I1rlmXi60z6vql8+1o4PXr0cKvLsl8KBqtzCj3EXUk3zWoKr+brqpWrCyt1AlUp+ZvZ6HRfi6aSqd/P6DIMt55A2s+g37Oknyk/v2pORzvo8uOjr/EObTSt3DEVXYbh1heo5Wc66/FXKddpjpes3yeVtl1uvGo2n3zyya6TOJ3r1MmPzqU6p6qmjnqg949TKbd8knH+HKrvB53D/c13dFlN898f/rvLT2/OLO1nXetVB4V+v/x2mnpVz87qYErf+er8TNucHnR4pX1T50tZkt9+pe8erTP+fe6PiaaWie5La5Sv315aU79c/DXNetJ6xLfV1Pss5RNdX5bjMrq8hqvdh/j60ryvhW2112xp9rct5/XXt0n3QfcM6vhNPzoFz7h0j4TSj7H6IVZ/euTOaaedlnR1FedLegz64yzrObrSDlRyyZL/NOeFSvvDeAQQQKC9ChAAba8l1473W4E2PbdNf6oVol7d/+u//suCjo7s3nvvdc/d0gWEeh/XL6mqPRJ/pqeyryZzCp6qyVylGodNMfXv399Njj7TLD6/f2aof85almXi60z6XhdP+sVWPVjGk98vvz/+NU1e1HOvgp8KdF588cWuKX10OwpSK/kL9+i0SsO+V1+/f/H5Ko2Pz8f7lhVI8hnMsgdqpqikXrwPO+ywLKtgmToTaInPdEsdf5XoWuP7RNsOOl1yAT09/zjoeKNRs2p/fk5zTo3nSbWQFNTUd6dq7JerGRd9Fp1aWLRE0veSaqCqFqiaiZf7QaPSdlX+aqKqZzLqT4EFtQ4JOv8r+31XaT3R8Vm+A/v16+dWoWuMckkBU9Um03wKYLd0+VZjGt3/rOtJ6xHdZnPDWcqnuXWmnd6W+1AL25a4ZktrWIv5/Y8y/kea+Dp13Z82aZ06p/jHgmgdCoqqcsVdd91lxxxzjGttlna9WebPeo6uxiVp/rOes7M4sAwCCCBQrwLJqsrV696zX+1GQDdkQS+N7hk90U6LVONDNcX07B4l39xSwwMHDtSL+0XXDcT+XXPNNfbzn/+8ZJnYLE2+9TVNVSPH1wyJLqD99B1n+OeQZlkmus60w3770eWCXidd8FfjBg0a5CZl2S8FkJWGDx/eKPip8S+++KJewppE7k0z/1TbSUn7Xc5UN7ukthHI8hlMu6d6vqfSM888436ciC+vThzU6dbxxx/van/Hp/O+/gRq9ZnOcvz5GjfVBAy9aNrvE7/t6GNT/LrKvQY9LLtnJ2ta0Nt9o+Cnajr6R0REb/yb2o6fFs2/bnR9XtSRULnkx1d6tmW5ZbKM8593/2NZfB36QVOdC6mlQTz5jo4U1PG1YrM2f9e6s3wH+v1XDTH92BhPOo+peb+a2GYt3/g6m3vv9ymLaXTdWdbjl0nioW354zPJZyRL+UTzU4vhttyHtLbl8tsS12zlttPS43zLpegjl/w2dZ5M80O5OvX5zne+YzfffLNfhXvVoz5+/OMfux9pdHz6R2qVzNRCb7Keo7O4pM2/Pw6rPb+0EB2rRQABBFpFgABoqzCzEdXQ1DP/9MzKcj3T+kCfaln4pIsapTvuuKPRxcu0adNcpxK9evUq6XTCL5vkdaeddnIBRPVaftVVV5UE+tQkbvz48e6mSDebvmOLLMsk2ZdK8/z61792nWj46dqvsWPHuuCinmvkO4vIsl+qpaekm7x4E0A1I5o4caKbrppGSZOs9NwlmSpAHb0x0vO3Jk2alHRVzFdjgSyfwbS7oKbv+rFAPx5cdtlljYLgOib07F/djPiahWm3wfytK1Crz3SW4883v09zQ1xJJ+33id+2gpVvv/12pdWG4zW/r8GjR7tEk34MUo1Q34Qxek5tajt+Wjz/vpd33fRHfzTUNufMmROeZ9XKoiWT3w+d131wxm9P+6wm/3oOarnnbOs7SzUV9V0zc+ZMV5M12uGQX0/S1yzfgToHqcbY0qVL3fdVNNCsQIya6SupWX7W8k26/36+akz9OvSaZT1pPLQNf3wm+YxkKR9to5apLfchrW25fLfENVu57bT0OH/d+pe//MX8s1G1TR1Ho0ePLrkWj+6Lfih58MEHS+4HdA+gjn1U21M/UkSTzoWqoa7jVC3FfCq3Hj+tVq/+85fmHJ3FJW3+/X5lOWfXyob1IIAAAm0tQBP4ti6Bgmxfzyk78cQT7fzzz7cJEybYQw895Goe6sZQzwJVcFTNRvRsMJ8USDvggANcE5bjjjvO1KxQN9GqMaIOe7RO1QBt7plmfn3xV9VeUE/yejaQmsiol151yKCbIAVktQ1dkIwbNy58dlSWZeLbTfpezTa1PdWW23nnnV2HSLpg1A2vHgmg8T5l2S/d1OkiSM3//DY6d+7sbmT1LCXVLpWBau0poOV/nfbbLPeqAICaCaqWnwLXemaabjD1eAM1JdR+l6tpU25djKutQJbPYJY9OP30011tb31Ojw46PlMNbx3LuunQjYr2Q8EgPXqBVP8CtfpMZzn+fIc9U6ZMcYFzBTCOPfbYTGhpv0/0vaLm5foxRzeN+n7So0LKPY5FO6SbbDXh1g26flBTQFA9nuscqse86DyrIIjOidGAalPbqZT/XXbZxT2vWd+jOnfrXK7HT+hHJtX+1A9a6oRs7733zmSVdCH94OG/o7W93XbbzT3vVK0HlGcFERV4OPzwwxutUvnWd7q+e5U0jw8gN5o5wYgs34FarZ7ZqmuTe+65xwVj9H2l4K3OX9p/leHIkSPdOSxL+SbY9ZJZqjGNrijrepJ6aFtNHbvRfdFw1vKJr6ea9229D2lsy+WzJa7Zym2npcepQoHOV6oRr34BdN2t4K7OGbrW1PtyrYV0La7HiOh+wNdk1Dnu7rvvdp/dgw46yFVW0HlWtZj9jzI6t/hgvfJWbj21znOWc3QWl7T5z3peqLUP60MAAQTaUoAAaFvqF2zbvifGG264wdUKUc0QJV2YKEiioEj8eWWjRo0yNeNTzTHdBPukWkmnnnpqWDPTj0/7OmTIELvppptcbTUF/Xyzbz2jbJ999nGdO8R7Ys+yTNr90vwKRqoG6M9+9jN3c6ZxuoBXk3VZ+eeSarxS2v1SczA1TVSNUuXb511loBtCXUwqmCUX3Qz6Joufbq3yf+2Xav9dcskl4UPoFfzQhZf2W51gkdpGIMtnMO2e6sZEtR7GjBnjgp6///3vw1UoqK7g+A477BCOY6D+BWr1mU57/Omc88QTT7jOchRI9DWgsoql/T45++yz3TlSQVA9b1oB/EoBUO2TflDTeXvq1KkuEKpgqIJ6X/7yl905UY8v0Q2/Ov9RMETnRaVK22kq//oxUTf+119/vdue1qMfGvR9+Y1vfMP9aVxLJ5l+6Utfct9VCsjqT0nBMdVAVfBYPdaXS9pPHwCtpvm7X3fa70Atp2sJXQOoqbt+jFUNdSWVm/ZfjzOQq1LW8nULp/hXjWl0M1nWk8ZD26p07Eb3ww9nKR+/bK1e23If0trG89xS12zx7bT0e533VDv8l7/8pfvMTQ86QFOSj35A1+ewXADUzRT717VrV/fZvfbaa92P7PqhVX9K6kdAn99oxYrY4i36Nu05OotLlvxnOS+0KBQrRwABBFpZoEPQRLWhlbfJ5gouoENONQL1TDjVKlQtSwX2mktaRg82V3M6BShrndQsUTVodLPmH5bf3DayLNPcOstN142zfvnWBbAueJpLafZLzY5UFvJVjaOmbvCb2258umrSqLaTLmzV3JFUHwJZP4Np91410fTYC9Xq0Oe2XIctadfJ/G0rUIvPdNrjT03HFYTU8aMAYy1Smu8TbVtBMHUgkSSplrtaNSiINmDAgMStFCptp7n8++9T9Taf5PshSR6yzKPmpvphU+d6fd6jta7KrU9B7aODWuKqJfqb3/ym3CyZx6X5DvQb0TIKcut6RDXUKu1/1vL120nzmta00rqzrCeph7ZZ6dittD9ZyqfSurKOb8t9SGMbz19LXrPFt9XS79X5mR6Jo06i4hUg0m7bPz9Un0/9EFtP15xpz9FZXLLkP8t5IW25MD8CCCBQbwIEQOutRNgfBBBAAAEEEEAg5wJqiqpHpZxxxhn2zW9+M+e5JXsIIIAAAggggAACbS1AE/i2LgG2jwACCCCAAAIIFEBANcJVi1ePNpg8ebKrVdvSzyotACtZRAABBBBAAAEEEEggQAA0ARKzIIAAAggggAACCFQncPXVV7tnoGoteubdmWeeWbGpeXVbYmkEEEAAAQQQQAABBEoFCICWevAOAQQQQAABBBBAoAUEhg4d6nps1jP/9t9/f9cBYgtshlUigAACCCCAAAIIINBIgGeANiJhBAIIIIAAAggggAACCCCAAAIIIIAAAgjkRaD5rrfzklPygQACCCCAAAIIIIAAAggggAACCCCAAAKFEyAAWrgiJ8MIIIAAAggggAACCCCAAAIIIIAAAggUR4AAaHHKmpwigAACCCCAAAIIIIAAAggggAACCCBQOAECoIUrcjKMAAIIIIAAAggggAACCCCAAAIIIIBAcQQIgBanrMkpAggggAACCCCAAAIIIIAAAggggAAChRMgAFq4IifDCCCAAAIIIIAAAggggAACCCCAAAIIFEegU3GySk4RQAABBBBAoIgCH3/8sT3//PM2d+5cW3/99W3gwIHWr18/W221dL8DNzQ02KxZs+yll16yvn372mabbWbrrbdeCem7775rf/3rX926tY3Bgwdbp05cbpUg8QYBBBBAAAEEEEAAgVYW4Iq8lcHZHAIIIIAAAgi0vMCiRYvs7LPPtr/85S82Z84cW7lyZclGFZw8/vjj7bjjjnPBzJKJZd6ce+65Nn78eFu8eHE49dRTT7WxY8e69/Pnz7eDDjrInn32WVu1alU4zyuvvGIbb7yxe3/mmWfaRRddFE5jAAEEEEAAAQQQQAABBFpHIF3Vh9bZJ7aCAAIIIIAAAghkFpgyZYpttdVWds0119jf//73RsFPrfjVV1+1c845xzbddFObNGlSk9uaOHGinXfeeSXBTy2gbSgtW7bMRowYYU8//XRJ8FNB1j59+rh5+IcAAggggAACCCCAAAJtJ0AAtO3s2TICCCCAAAII1Fjg5JNPtv3339/eeOMNt2YN//a3v7XHH3/c1Dx99uzZdtttt9luu+3mpn/44Yd2+OGH21lnnVVxT6677jo3rXPnznbttdfaa6+9Zv/617/ccprw2GOPuZqfGt53333tz3/+s33wwQf25JNPWocOHaxr167uj6bwEiIhgAACCCCAAAIIIND6Ah2C51k1tP5m2SICCCCAAAIIIFBbgbvuuss1Q9da9WzOK6+80g499NCKG9H8Rx55pKvBqZnuuOMOO/jggxvNr1qiauKu4ObUqVMbTb/pppvsu9/9rhuv538OGzas0TyMQAABBBBAAAEEEEAAgbYToAZo29mzZQQQQAABBBCokYBqcp522mlubappOX369CaDn5rxwAMPtPvvvz/sDOmHP/yhvfXWW432SLU9lTbYYING0zTCT9dwpXk0jYQAAggggAACCCCAAAJtI0AnSG3jzlYRQAABBBBAoIYC6lxowYIFbo2nnHKKbbHFFonWvvPOO9uPfj386t4AAAq9SURBVPQjV1tUgUw1d1dzeDVhV8/xSp988ol7VcdKau6upN7kly9fbu+9956poyOf1AmSb34/dOhQF1x94okn3OQNN9zQ+vfv72dt9KoOm/Qc0ddff911zDRo0CDbc889TU3vm0rah2eeecb9aXibbbaxbbfdNux8qallmYYAAggggAACCCCAQBEEaAJfhFImjwgggAACCORcYM0113TP+FxnnXVs3rx51qtXr8Q5VsBSwUk9FWjIkCGu46SZM2fajjvuWHEdJ510kr3wwgv24IMPVpxHAdUuXbqE+1KpF3jVQlWP8i+++GKjdW2yySZ2ySWX2MiRIxtN0/6OGTPGBWw/+uijRtP1bNOrrrqKjpgayTACAQQQQAABBBBAoGgC1AAtWomTXwQQQAABBHImoGbr6uBI6Stf+UoYcEyazb59+9oOO+xgjzzyiM2aNcsFInv06OHWpXU89dRTrnd3BVcHDhzoVrvxxhu718WLF9ubb75pCxcudO9V+1JBT6UknR5dffXVpo6bVq1aZR07dnQ1N9W7vAKwL730knv26GGHHeamHXTQQW69+qcm/+rgadq0aW6caqTq2aO9e/d2nS+p9uott9xiqlX6wAMP2ODBg8NlGUAAAQQQQAABBBBAoGgCBECLVuLkFwEEEEAAgZwJqCamT+qwKEsaPny4C4Bq2blz59p+++3neo7X+549e7om8QcccIDrUV7jounyyy+3UaNGuVFTpkwpaea+ZMmS6Kwlw3PmzLHTTz/dBT/79etnkyZNMjXJ9+nRRx+1vfbay7SOo446yr72ta+Fwd3LLrssDH6ecMIJpvfdu3f3i9qtt95qxx57rGuerwCrgqAkBBBAAAEEEEAAAQSKKkAnSEUtefKNAAIIIIBATgSiTcfVZDxLUg1Kn1577TU/2KKvF1xwgfmm6/HgpzasGp1nnHGG24elS5fanXfe6YZV21TN4pW+/vWvm2qRRoOfGv+tb33LrrnmGg26ZvqTJ092w/xDAAEEEEAAAQQQQKCIAgRAi1jq5BkBBBBAAIEcCaipuE++ibp/n/S1T58+4azRXt3DkTUeWLlypam2qNLuu+9eUvMzuinVEFWnTmPHjrXtttvOTZowYYItW7bMDZ9//vnR2UuGjzjiCNdZk0ZqGRICCCCAAAIIIIAAAkUVoAl8UUuefCOAAAIIIJATga5du4Y5ef/998PhNAP//Oc/w9nXXnvtcLilBmbPnh0+t7Spzpa6detm48aNK9kNH/BVvgcMGGB6DmmlpOeJ6hmlfplK8zEeAQQQQAABBBBAAIE8CxAAzXPpkjcEEEAAAQQKILDRRhuFuYw+DzQcmWBAz/30qX///n6wxV7V87xPvkMl/765V9/kf/ny5bbeeus1N7ubPm/ePNfLfYcOHRLNz0wIIIAAAggggAACCORJgCbweSpN8oIAAggggEABBQYNGhTm2gcHwxEJB6IB0KzPEU24KTdbtHOktdZaK82iFq2tmnRB9RrfGk37k+4P8yGAAAIIIIAAAggg0JoC1ABtTW22hQACCCCAAAI1F1AT8r59+5pqVT788MOmZvC9evVKvJ1//OMf9swzz7j5t9hiC9t8880TL5t1xmitz2ht0CTrUw1VBUG1r+opPmnq0aNH0lmZDwEEEEAAAQQQQACBXAlQAzRXxUlmEEAAAQQQKJ5Ax44dXa/nyvnbb79t5557bioE9bTue2M/8sgjUy2bdeYvfOEL4aLN1ei84YYb7Fe/+pVNmzbNLeNrvOq5np07d7aePXsm+qP5e0jOAAIIIIAAAggggEDBBAiAFqzAyS4CCCCAAAJ5FPjJT35ivXv3dlm78sor7dlnn02UTQUVb7/9djevmqJ///vfT7RctTOp1/l1113XrWbq1Knu+Zzl1rlq1SpTgPbkk0+2a6+91s3ia6iqJ/n77ruv3GJunJZVD/O77rqrnXrqqRXnYwICCCCAAAIIIIAAAnkXIACa9xImfwgggAACCBRAQM3Cx4wZ43KqwOBOO+1kN998c5M5Hz9+vO23337hPAowrr/++uH7lh5QUFNJze/vvvvusptTHhYtWuSmHXjgge5VQVrfxP/000+3pUuXll1WNUenT59uM2bMsC5dupSdh5EIIIAAAggggAACCBRBgABoEUqZPCKAAAIIIFAAge9973umgKCSOhk66qijbMSIEXbdddfZk08+aYsXL7ZHHnnErr76attrr71crUjf9F21LA8++OBWVRo1apT5Z4EeccQRduONN5qCtz5NnDjR1fzU+6FDh9oBBxzgJilIe95557nhBQsW2Pbbb2+PPfaYe69/L7/8sl166aV24oknunGqbeqDreFMDCCAAAIIIIAAAgggUCABOkEqUGGTVQQQQAABBPIucPnll9tWW21lJ5xwgnuu5+TJk01/lZI6Bvrtb39rhx12WKVZWmz8GmusYQpyatuvvfaaKYCroKWe8blw4UJ799133bbVVP7OO++0rl27hvty0kkn2fPPP++Cu3odNmyYrbnmmrbOOuuYng3qU7du3WzKlClhoNWP5xUBBBBAAAEEEEAAgSIJUAO0SKVNXhFAAAEEECiAwDHHHGPq2X306NEVm7RvsskmduGFF9qLL77YJsFPXwxqqv/cc8/Z4YcfbgpWLl++3L1X8FOdO/3gBz+wWbNm2cCBA/0i7rVTp07umaB//OMfbciQIbbaaqu5Gq4++Kll1aHT3/72Nxs+fHjJsrxBAAEEEEAAAQQQQKBoAh0aglS0TJNfBBBAAAEEECiGgC5zVLty/vz59uabb9oGG2xgAwYMsA033NDqrVd0dVo0d+5cF5Tt16+fDR482AVFk5TUsmXLbPbs2S6vala/6aabup7hkyzLPAgggAACCCCAAAII5F2AAGjeS5j8IYAAAggggAACCCCAAAIIIIAAAgggUGABmsAXuPDJOgIIIIAAAggggAACCCCAAAIIIIAAAnkXIACa9xImfwgggAACCCCAAAIIIIAAAggggAACCBRYgABogQufrCOAAAIIIIAAAggggAACCCCAAAIIIJB3AQKgeS9h8ocAAggggAACCCCAAAIIIIAAAggggECBBQiAFrjwyToCCCCAAAIIIIAAAggggAACCCCAAAJ5FyAAmvcSJn8IIIAAAggggAACCCCAAAIIIIAAAggUWIAAaIELn6wjgAACCCCAAAIIIIAAAggggAACCCCQdwECoHkvYfKHAAIIIIAAAggggAACCCCAAAIIIIBAgQUIgBa48Mk6AggggAACCCCAAAIIIIAAAggggAACeRcgAJr3EiZ/CCCAAAIIIIAAAggggAACCCCAAAIIFFiAAGiBC5+sI4AAAggggAACCCCAAAIIIIAAAgggkHcBAqB5L2HyhwACCCCAAAIIIIAAAggggAACCCCAQIEFCIAWuPDJOgIIIIAAAggggAACCCCAAAIIIIAAAnkXIACa9xImfwgggAACCCCAAAIIIIAAAggggAACCBRYgABogQufrCOAAAIIIIAAAggggAACCCCAAAIIIJB3AQKgeS9h8ocAAggggAACCCCAAAIIIIAAAggggECBBQiAFrjwyToCCCCAAAIIIIAAAggggAACCCCAAAJ5FyAAmvcSJn8IIIAAAggggAACCCCAAAIIIIAAAggUWOD/AfMRON0faaAYAAAAAElFTkSuQmCC" width="672" /></p>
<pre class="r"><code>##################################################################

# Figure 2. Proportion of Democrats and Republicans choosing &quot;partisan election&quot; #

##################################################################

sbPlotP &lt;- cces2.2
sbPlotP$pid &lt;- factor(sbPlotP$pid)
sbPlotP &lt;- summarySE(cces2.2, measurevar=&quot;RCO322_p&quot;, groupvars=c(&quot;pid&quot;), na.rm=TRUE)
sbPlotP$office &lt;- &quot;school board&quot;
sbPlotP$proportion &lt;- sbPlotP$RCO322_p
sbPlotP$RCO322_p &lt;- NULL

sb_party_PlotP &lt;- ggplot(sbPlotP, aes(x=pid, y=proportion, fill=pid)) 
sb_party_PlotP + 
  scale_fill_manual(values=c(&quot;#00AFBB&quot;, &quot;#FC4E07&quot;),
                    name=&quot;Party&quot;, # Legend label, use darker colors
                    breaks=c(&quot;Democrat&quot;, &quot;Republican&quot;),
                    labels=c(&quot;Democrat&quot;, &quot;Republican&quot;)) +
  geom_bar(position=position_dodge(), stat=&quot;identity&quot;,
           colour=&quot;black&quot;, # Use black outlines,
           size=.3) +      # Thinner lines
  geom_errorbar(aes(ymin=proportion-se, ymax=proportion+se),
                size=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9))+
  xlab(&quot;&quot;) +
  scale_x_discrete(breaks=c(&quot;Democrat&quot;,&quot;Republican&quot;),
                   labels=c(&quot;&quot;, &quot;&quot;)) +
  ylab(&quot;proportion&quot;) +
  ylim(0,0.8) +
  ggtitle(&quot;&quot;) +
  theme_bw()</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>#leo

leoPlotP &lt;- cces2.2
leoPlotP$pid &lt;- factor(leoPlotP$pid)
leoPlotP &lt;- summarySE(cces2.2, measurevar=&quot;RCO321_p&quot;, groupvars=c(&quot;pid&quot;), na.rm=TRUE)
leoPlotP$office &lt;- &quot;local election official&quot;
leoPlotP$proportion &lt;- leoPlotP$RCO321_p
leoPlotP$RCO321_p &lt;- NULL

leo_party_PlotP &lt;- ggplot(leoPlotP, aes(x=pid, y=proportion, fill=pid)) 
leo_party_PlotP + 
  scale_fill_manual(values=c(&quot;#00AFBB&quot;, &quot;#FC4E07&quot;),
                    name=&quot;Party&quot;, # Legend label, use darker colors
                    breaks=c(&quot;Democrat&quot;, &quot;Republican&quot;),
                    labels=c(&quot;Democrat&quot;, &quot;Republican&quot;)) +
  geom_bar(position=position_dodge(), stat=&quot;identity&quot;,
           colour=&quot;black&quot;, # Use black outlines,
           size=.3) +      # Thinner lines
  geom_errorbar(aes(ymin=proportion-se, ymax=proportion+se),
                size=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9))+
  xlab(&quot;&quot;) +
  scale_x_discrete(breaks=c(&quot;Democrat&quot;,&quot;Republican&quot;),
                   labels=c(&quot;&quot;, &quot;&quot;)) +
  ylab(&quot;proportion&quot;) +
  ylim(0,0.8) +
  ggtitle(&quot;&quot;) +
  theme_bw()</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>#sheriff

sherPlotP &lt;- cces2.2
sherPlotP$pid &lt;- factor(sherPlotP$pid)
sherPlotP &lt;- summarySE(cces2.2, measurevar=&quot;RCO323_p&quot;, groupvars=c(&quot;pid&quot;), na.rm=TRUE)
sherPlotP$office &lt;- &quot;sheriff&quot;
sherPlotP$proportion &lt;- sherPlotP$RCO323_p
sherPlotP$RCO323_p &lt;- NULL

sher_party_PlotP &lt;- ggplot(sherPlotP, aes(x=pid, y=proportion, fill=pid)) 
sher_party_PlotP + 
  scale_fill_manual(values=c(&quot;#00AFBB&quot;, &quot;#FC4E07&quot;),
                    name=&quot;Party&quot;, # Legend label, use darker colors
                    breaks=c(&quot;Democrat&quot;, &quot;Republican&quot;),
                    labels=c(&quot;Democrat&quot;, &quot;Republican&quot;)) +
  geom_bar(position=position_dodge(), stat=&quot;identity&quot;,
           colour=&quot;black&quot;, # Use black outlines,
           size=.3) +      # Thinner lines
  geom_errorbar(aes(ymin=proportion-se, ymax=proportion+se),
                size=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9))+
  xlab(&quot;&quot;) +
  scale_x_discrete(breaks=c(&quot;Democrat&quot;,&quot;Republican&quot;),
                   labels=c(&quot;&quot;, &quot;&quot;)) +
  ylab(&quot;proportion&quot;) +
  ylim(0,0.8) +
  ggtitle(&quot;&quot;) +
  theme_bw()</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>#da

daPlotP &lt;- cces2.2
daPlotP$pid &lt;- factor(daPlotP$pid)
daPlotP &lt;- summarySE(cces2.2, measurevar=&quot;RCO324_p&quot;, groupvars=c(&quot;pid&quot;), na.rm=TRUE)
daPlotP$office &lt;- &quot;district attorney&quot;
daPlotP$proportion &lt;- daPlotP$RCO324_p
daPlotP$RCO324_p &lt;- NULL

da_party_PlotP &lt;- ggplot(daPlotP, aes(x=pid, y=proportion, fill=pid)) 
da_party_PlotP + 
  scale_fill_manual(values=c(&quot;#00AFBB&quot;, &quot;#FC4E07&quot;),
                    name=&quot;Party&quot;, # Legend label, use darker colors
                    breaks=c(&quot;Democrat&quot;, &quot;Republican&quot;),
                    labels=c(&quot;Democrat&quot;, &quot;Republican&quot;)) +
  geom_bar(position=position_dodge(), stat=&quot;identity&quot;,
           colour=&quot;black&quot;, # Use black outlines,
           size=.3) +      # Thinner lines
  geom_errorbar(aes(ymin=proportion-se, ymax=proportion+se),
                size=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9))+
  xlab(&quot;&quot;) +
  scale_x_discrete(breaks=c(&quot;Democrat&quot;,&quot;Republican&quot;),
                   labels=c(&quot;&quot;, &quot;&quot;)) +
  ylab(&quot;proportion&quot;) +
  ylim(0,0.8) +
  ggtitle(&quot;&quot;) +
  theme_bw()</code></pre>
<p><img 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qgkQIAAAQIECBAgQIAAAQIECBAgQKAOAgLQOoyiayBAgAABAgQIECBAgAABAgQIECBAoFBAAFrIopIAAQIECBAgQIAAAQIECBAgQIAAgToICEDrMIqugQABAgQIECBAgAABAgQIECBAgACBQgEBaCGLSgIECBAgQIAAAQIECBAgQIAAAQIE6iAgAK3DKLoGAgQIECBAgAABAgQIECBAgAABAgQKBQSghSwqCRAgQIAAAQIECBAgQIAAAQIECBCog4AAtA6j6BoIECBAgAABAgQIECBAgAABAgQIECgU6F5Y24krZ82alcaMGZMefvjhNGnSpDR06NA0bNiwNGrUqNStW7fFvrJo57bbbksvv/xydv7gwYPTnnvumTbddNPFbssJBAgQIECAAAECBAgQIECAAAECBAgsXYFaBaDvvvtuOvDAA9Mrr7ySKa688spZeBkB5gMPPJBOOOGE1LNnzzYLn3vuuem6667Lju/Vq1eaP39+Gj9+fBo7dmzaf//901e/+tU2t+VAAgQIECBAgAABAgQIECBAgAABAgSWvkCtboE/5ZRTsvBzxIgR6eabb0433nhjuvrqq9OQIUPSvffem84///w2C999991Z+BmB6Xe/+92srd/85jfpkEMOydq45JJLslmmbW7QgQQIECBAgAABAgQIECBAgAABAgQILHWB2gSgMTPzwQcfTL17906nnnpq6t+/f4a59tprp7PPPju7fT1mbk6dOrVNyHfccUd23E477ZT22GOP1Ldv37TiiiumvfbaK40cOTLbFzNLFQIECBAgQIAAAQIECBAgQIAAAQIEll2B2gSg99xzT6a8ww47pBVWWKGZeNwKv/XWW6f3338/u3292c5WXrz++uvZnjivZdluu+2yqtdee63lLq8JECBAgAABAgQIECBAgAABAgQIEFiGBGoTgD711FMZa9z+XlTyIPOJJ54o2r1AXSycFCUPVhsPuP/++7OXW221VWO1bQIECBAgQIAAAQIECBAgQIAAAQIEljGB2iyClM/GHDBgQCFxXp8vkFR4UEPlLrvskm655ZZ03333pauuuirtuuuu2SJIcRv9uHHjUp8+fdLOO+/ccMaCm3Hc7373uwV3/E9N167/mz2/9957afLkyYXHqCRAgEBLgTlz5jRVzZw5M82dO7fptQ0CBAgQILC8CTT+ORiPuurSpcvyRuB6CRBop0DcITpv3rx2nu00AgQ6m0BtAtAIEqPkQWfLgVhppZWyqvy4lvtbvt50003Tz372s2zl+Isvvjj99Kc/zQ6JH5CbbLJJOumkk9Jaa63V8rRmr59++un0y1/+slld/mLNNdfMNmfNmpWmT5+eV/tOgACBNgvMnj07xZdCgAABAgQIpDRjxgwMBAgQaLNATCyYP39+m493IAECnVugFrfARygZM6GixEJFRaVfv35ZdQSObS2PPfZYymeWDho0KK266qrZqfF80Oeff76tzTiOAAECBAgQIECAAAECBAgQIECAAIGKBGoxAzRuJ4/V3+N/fVsLOPP6nj17ton6mGOOyW5/32yzzdLo0aPTOuusk50Xwecpp5ySjj322PT5z38+HX744a22N2rUqGy2aNEBcdv7oYcemgW2sUiTQoAAgbYIxM+yfCZ7/NyLL4UAAQIECCyvAlOmTEn542HiTrD8MVPLq4frJkCg7QK9evXyM6PtXI4k0OkFahGAxijE7Mx4vmc8+6eo5PV9+/Yt2t2s7tFHH83Czzj2tNNOa5r5GQdttNFG6cwzz0z77rtvuv7669Puu++e1TVr4B8v4jb3/Fb3lvsnTpyYVfXo0SPFD16FAAECbRFofNZZ9+7d/fxoC5pjCBAgQKC2Ao3P/IyJDt26davttbowAgQ6ViB+XjT+DOnY1rVGgMCyJlCLW+ADNb89PQ86W0LH/w5HGThwYMtdC7yeMGFCVjd8+PCmdhsPilAzXwH+8ccfb9xlmwABAgQIECBAgAABAgQIECBAgACBZUigNgHo6quvnrG++OKLhbx5fSxutKiS30YTszNbK/miSrFynEKAAAECBAgQIECAAAECBAgQIECAwLIpUJsAdMcdd8yE77zzzgWkY5Gku+66K6sfNmzYAvtbVgwdOjSreuKJJ1Kc27JEXazwHiVuiVcIECBAgAABAgQIECBAgAABAgQIEFg2BWoTgI4cOTINHjw4Pffcc2ns2LHNtK+66qr0zjvvpPXXXz+NGDGi2b4//vGP6Y477kgvvfRSU/0HP/jBtMYaa2Tn/PCHP0zz589v2hcbl112WXr11VfTWmutlbbccstm+7wgQIAAAQIECBAgQIAAAQIECBAgQGDZEajNIkjx8OIDDjggHX/88en0009P48aNSzGT88knn8y243b2o446aoGHHJ933nnp9ddfz87dYIMNspHp06dPtsp7rPB+8803Z7M9P/rRj6YVVlghPfzww1mbsfhIrAQfdQoBAgQIECBAgAABAgQIECBAgAABAsumQG0C0ODdfvvt0znnnJMFoHfffXeKrygxMzTCzHzhoqxyEb/EAkiXX355Ovvss9Njjz2Wnn/++aYzPvShD6XvfOc7ab311muqs0GAAAECBAgQIECAAAECBAgQIECAwLInUKsANHgjuLz22muz29dfeeWVFIsjDRo0KHXtWny3/zXXXNPqqMSM0AsuuCBNnz49TZw4MZs9GqFn7969Wz3HDgIECBAgQIAAAQIECBAgQIAAAQIElh2B2gWgOe0qq6yS4qsjStwSv8kmm3REU9ogQIAAAQIECBAgQIAAAQIECBAgQGApChRPi1yKHfBWBAgQIECAAAECBAgQIECAAAECBAgQKEtAAFqWrHYJECBAgAABAgQIECBAgAABAgQIEKhcQABa+RDoAAECBAgQIECAAAECBAgQIECAAAECZQkIQMuS1S4BAgQIECBAgAABAgQIECBAgAABApULCEArHwIdIECAAAECBAgQIECAAAECBAgQIECgLAEBaFmy2iVAgAABAgQIECBAgAABAgQIECBAoHIBAWjlQ6ADBAgQIECAAAECBAgQIECAAAECBAiUJSAALUtWuwQIECBAgAABAgQIECBAgAABAgQIVC4gAK18CHSAAAECBAgQIECAAAECBAgQIECAAIGyBASgZclqlwABAgQIECBAgAABAgQIECBAgACBygUEoJUPgQ4QIECAAAECBAgQIECAAAECBAgQIFCWgAC0LFntEiBAgAABAgQIECBAgAABAgQIECBQuYAAtPIh0AECBAgQIECAAAECBAgQIECAAAECBMoSEICWJatdAgQIECBAgAABAgQIECBAgAABAgQqFxCAVj4EOkCAAAECBAgQIECAAAECBAgQIECAQFkCAtCyZLVLgAABAgQIECBAgAABAgQIECBAgEDlAgLQyodABwgQIECAAAECBAgQIECAAAECBAgQKEtAAFqWrHYJECBAgAABAgQIECBAgAABAgQIEKhcQABa+RDoAAECBAgQIECAAAECBAgQIECAAAECZQkIQMuS1S4BAgQIECBAgAABAgQIECBAgAABApULCEArHwIdIECAAAECBAgQIECAAAECBAgQIECgLAEBaFmy2iVAgAABAgQIECBAgAABAgQIECBAoHIBAWjlQ6ADBAgQIECAAAECBAgQIECAAAECBAiUJSAALUtWuwQIECBAgAABAgQIECBAgAABAgQIVC4gAK18CHSAAAECBAgQIECAAAECBAgQIECAAIGyBASgZclqlwABAgQIECBAgAABAgQIECBAgACBygUEoJUPgQ4QIECAAAECBAgQIECAAAECBAgQIFCWgAC0LFntEiBAgAABAgQIECBAgAABAgQIECBQuYAAtPIh0AECBAgQIECAAAECBAgQIECAAAECBMoSEICWJatdAgQIECBAgAABAgQIECBAgAABAgQqFxCAVj4EOkCAAAECBAgQIECAAAECBAgQIECAQFkCAtCyZLVLgAABAgQIECBAgAABAgQIECBAgEDlAgLQyodABwgQIECAAAECBAgQIECAAAECBAgQKEtAAFqWrHYJECBAgAABAgQIECBAgAABAgQIEKhcQABa+RDoAAECBAgQIECAAAECBAgQIECAAAECZQkIQMuS1S4BAgQIECBAgAABAgQIECBAgAABApULCEArHwIdIECAAAECBAgQIECAAAECBAgQIECgLAEBaFmy2iVAgAABAgQIECBAgAABAgQIECBAoHIBAWjlQ6ADBAgQIECAAAECBAgQIECAAAECBAiUJSAALUtWuwQIECBAgAABAgQIECBAgAABAgQIVC4gAK18CHSAAAECBAgQIECAAAECBAgQIECAAIGyBASgZclqlwABAgQIECBAgAABAgQIECBAgACBygUEoJUPgQ4QIECAAAECBAgQIECAAAECBAgQIFCWgAC0LFntEiBAgAABAgQIECBAgAABAgQIECBQuYAAtPIh0AECBAgQIECAAAECBAgQIECAAAECBMoSEICWJatdAgQIECBAgAABAgQIECBAgAABAgQqFxCAVj4EOkCAAAECBAgQIECAAAECBAgQIECAQFkCAtCyZLVLgAABAgQIECBAgAABAgQIECBAgEDlAgLQyodABwgQIECAAAECBAgQIECAAAECBAgQKEtAAFqWrHYJECBAgAABAgQIECBAgAABAgQIEKhcQABa+RDoAAECBAgQIECAAAECBAgQIECAAAECZQkIQMuS1S4BAgQIECBAgAABAgQIECBAgAABApULCEArHwIdIECAAAECBAgQIECAAAECBAgQIECgLAEBaFmy2iVAgAABAgQIECBAgAABAgQIECBAoHIBAWjlQ6ADBAgQIECAAAECBAgQIECAAAECBAiUJSAALUtWuwQIECBAgAABAgQIECBAgAABAgQIVC4gAK18CHSAAAECBAgQIECAAAECBAgQIECAAIGyBASgZclqlwABAgQIECBAgAABAgQIECBAgACBygUEoJUPgQ4QIECAAAECBAgQIECAAAECBAgQIFCWgAC0LFntEiBAgAABAgQIECBAgAABAgQIECBQuYAAtPIh0AECBAgQIECAAAECBAgQIECAAAECBMoSEICWJatdAgQIECBAgAABAgQIECBAgAABAgQqFxCAVj4EOkCAAAECBAgQIECAAAECBAgQIECAQFkCAtCyZLVLgAABAgQIECBAgAABAgQIECBAgEDlAgLQyodABwgQIECAAAECBAgQIECAAAECBAgQKEtAAFqWrHYJECBAgAABAgQIECBAgAABAgQIEKhcQABa+RDoAAECBAgQIECAAAECBAgQIECAAAECZQkIQMuS1S4BAgQIECBAgAABAgQIECBAgAABApULCEArHwIdIECAAAECBAgQIECAAAECBAgQIECgLAEBaFmy2iVAgAABAgQIECBAgAABAgQIECBAoHIBAWjlQ6ADBAgQIECAAAECBAgQIECAAAECBAiUJSAALUtWuwQIECBAgAABAgQIECBAgAABAgQIVC4gAK18CHSAAAECBAgQIECAAAECBAgQIECAAIGyBASgZclqlwABAgQIECBAgAABAgQIECBAgACBygUEoJUPgQ4QIECAAAECBAgQIECAAAECBAgQIFCWgAC0LFntEiBAgAABAgQIECBAgAABAgQIECBQuYAAtPIh0AECBAgQIECAAAECBAgQIECAAAECBMoSEICWJatdAgQIECBAgAABAgQIECBAgAABAgQqFxCAVj4EOkCAAAECBAgQIECAAAECBAgQIECAQFkCAtCyZLVLgAABAgQIECBAgAABAgQIECBAgEDlAt0r78Fy2oH58+dnV/7++++nGTNmLKcKLpsAgcUViJ8ZeZk9e7afHzmG7wQIECCwXArMmzev6bpnzpyZunY1v6MJxAYBAgsVmDNnTsr/Xb7QA+0kQKAWAgLQiodx2rRp6d133624F96eAIHOKBD/0IsvhQABAgQIEEhpypQpGAgQINBmgZhY0PifKG0+0YEECHRKAQFoRcPWpUuX7J0HDhyY1lhjjYp64W0JEOhsAjFjPP8HXr9+/VLfvn072yXoLwECBAgQ6DCBv//97ynuiIiy2mqrmQHaYbIaIlB/gd69e6du3brV/0JdIQECmYAAtOIPQgShbtWpeBC8PYFOJJD/50l02c+PTjRwukqAAAECpQv4c7F0Ym9AoFYCjX+vrtWFuRgCBAoFPCSnkEUlAQIECBAgQIAAAQIECBAgQIAAAQJ1EBCA1mEUXQMBAgQIECBAgAABAgQIECBAgAABAoUCAtBCFpUECBAgQIAAAQIECBAgQIAAAQIECNRBQABah1F0DQQIECBAgAABAgQIECBAgAABAgQIFAoIQAtZVBIgQIAAAQIECBAgQIAAAQIECBAgUAcBAWgdRtE1ECBAgAABAgQIECBAgAABAgQIECBQKCAALWRRSYAAAQIECBAgQIAAAQIECBAgQIBAHQQEoHUYRddAgAABAgQIECBAgAABAgQIECBAgEChgAC0kEUlAQIECBAgQIAAAQIECBAgQIAAAQJ1EBCA1mEUXQMBAgQIECBAgAABAgQIECBAgAABAoUCAtBCFpUECBAgQIAAAQIECBAgQIAAAQIECNRBQABah1F0DQQIECBAgAABAgQIECBAgAABAgQIFAoIQAtZVBIgQIAAAQIECBAgQIAAAQIECBAgUAcBAWgdRtE1ECBAgAABAgQIECBAgAABAgQIECBQKCAALWRRSYAAAQIECBAgQIAAAQIECBAgQIBAHQQEoHUYRddAgAABAgQIECBAgAABAgQIECBAgEChgAC0kEUlAQIECBAgQIAAAQIECBAgQIAAAQJ1EBCA1mEUXQMBAgQIECBAgAABAgQIECBAgAABAoUCAtBCFpUECBAgQIAAAQIECBAgQIAAAQIECNRBQABah1F0DQQIECBAgAABAgQIECBAgAABAgQIFAoIQAtZVBIgQIAAAQIECBAgQIAAAQIECBAgUAcBAWgdRtE1ECBAgAABAgQIECBAgAABAgQIECBQKCAALWRRSYAAAQIECBAgQIAAAQIECBAgQIBAHQQEoHUYRddAgAABAgQIECBAgAABAgQIECBAgEChgAC0kEUlAQIECBAgQIAAAQIECBAgQIAAAQJ1EBCA1mEUXQMBAgQIECBAgAABAgQIECBAgAABAoUCAtBCFpUECBAgQIAAAQIECBAgQIAAAQIECNRBQABah1F0DQQIECBAgAABAgQIECBAgAABAgQIFAoIQAtZVBIgQIAAAQIECBAgQIAAAQIECBAgUAcBAWgdRtE1ECBAgAABAgQIECBAgAABAgQIECBQKCAALWRRSYAAAQIECBAgQIAAAQIECBAgQIBAHQQEoHUYRddAgAABAgQIECBAgAABAgQIECBAgEChgAC0kEUlAQIECBAgQIAAAQIECBAgQIAAAQJ1EBCA1mEUXQMBAgQIECBAgAABAgQIECBAgAABAoUCAtBCFpUECBAgQIAAAQIECBAgQIAAAQIECNRBQABah1F0DQQIECBAgAABAgQIECBAgAABAgQIFAoIQAtZVBIgQIAAAQIECBAgQIAAAQIECBAgUAcBAWgdRtE1ECBAgAABAgQIECBAgAABAgQIECBQKCAALWRRSYAAAQIECBAgQIAAAQIECBAgQIBAHQQEoHUYRddAgAABAgQIECBAgAABAgQIECBAgEChgAC0kEUlAQIECBAgQIAAAQIECBAgQIAAAQJ1EBCA1mEUXQMBAgQIECBAgAABAgQIECBAgAABAoUCAtBCFpUECBAgQIAAAQIECBAgQIAAAQIECNRBQABah1F0DQQIECBAgAABAgQIECBAgAABAgQIFAoIQAtZVBIgQIAAAQIECBAgQIAAAQIECBAgUAcBAWgdRtE1ECBAgAABAgQIECBAgAABAgQIECBQKCAALWRRSYAAAQIECBAgQIAAAQIECBAgQIBAHQQEoHUYRddAgAABAgQIECBAgAABAgQIECBAgEChgAC0kEUlAQIECBAgQIAAAQIECBAgQIAAAQJ1EBCA1mEUXQMBAgQIECBAgAABAgQIECBAgAABAoUCAtBCFpUECBAgQIAAAQIECBAgQIAAAQIECNRBQABah1F0DQQIECBAgAABAgQIECBAgAABAgQIFAoIQAtZVBIgQIAAAQIECBAgQIAAAQIECBAgUAcBAWgdRtE1ECBAgAABAgQIECBAgAABAgQIECBQKCAALWRRSYAAAQIECBAgQIAAAQIECBAgQIBAHQQEoHUYRddAgAABAgQIECBAgAABAgQIECBAgEChgAC0kEUlAQIECBAgQIAAAQIECBAgQIAAAQJ1EBCA1mEUXQMBAgQIECBAgAABAgQIECBAgAABAoUCAtBCFpUECBAgQIAAAQIECBAgQIAAAQIECNRBQABah1F0DQQIECBAgAABAgQIECBAgAABAgQIFAoIQAtZVBIgQIAAAQIECBAgQIAAAQIECBAgUAcBAWgdRtE1ECBAgAABAgQIECBAgAABAgQIECBQKCAALWRRSYAAAQIECBAgQIAAAQIECBAgQIBAHQQEoHUYRddAgAABAgQIECBAgAABAgQIECBAgEChgAC0kEUlAQIECBAgQIAAAQIECBAgQIAAAQJ1EBCA1mEUXQMBAgQIECBAgAABAgQIECBAgAABAoUCAtBCFpUECBAgQIAAAQIECBAgQIAAAQIECNRBQABah1F0DQQIECBAgAABAgQIECBAgAABAgQIFAoIQAtZVBIgQIAAAQIECBAgQIAAAQIECBAgUAcBAWgdRtE1ECBAgAABAgQIECBAgAABAgQIECBQKCAALWRRSYAAAQIECBAgQIAAAQIECBAgQIBAHQQEoHUYRddAgAABAgQIECBAgAABAgQIECBAgEChgAC0kEUlAQIECBAgQIAAAQIECBAgQIAAAQJ1EBCA1mEUXQMBAgQIECBAgAABAgQIECBAgAABAoUCAtBCFpUECBAgQIAAAQIECBAgQIAAAQIECNRBQABah1F0DQQIECBAgAABAgQIECBAgAABAgQIFAoIQAtZVBIgQIAAAQIECBAgQIAAAQIECBAgUAcBAWgdRtE1ECBAgAABAgQIECBAgAABAgQIECBQKCAALWRRSYAAAQIECBAgQIAAAQIECBAgQIBAHQQEoHUYRddAgAABAgQIECBAgAABAgQIECBAgEChgAC0kEUlAQIECBAgQIAAAQIECBAgQIAAAQJ1EBCA1mEUXQMBAgQIECBAgAABAgQIECBAgAABAoUC3QtrO3HlrFmz0pgxY9LDDz+cJk2alIYOHZqGDRuWRo0albp167bYVxZt/Pa3v03PPPNMeu2119KgQYPSyJEj0+677566d68d32L7OIEAAQIECBAgQIAAAQIECBAgQIDAsixQqwTv3XffTQceeGB65ZVXMvOVV1453XbbbdnXAw88kE444YTUs2fPNo/HU089lUaPHp3eeuut1KVLl7TKKqukF198MUVbY8eOTRdccEHq1atXm9tzIAECBAgQIECAAAECBAgQIECAAAECS1egVrfAn3LKKVn4OWLEiHTzzTenG2+8MV199dVpyJAh6d57703nn39+m3WnTJmSDj/88Cz8/NKXvpS1dcMNN6Qrr7wyrb/++mnChAnpoosuanN7DiRAgAABAgQIECBAgAABAgQIECBAYOkL1CYAHT9+fHrwwQdT796906mnnpr69++faa699trp7LPPzm5/j1mbU6dObZPyddddl2bMmJEiTD3ooIPSwIEDs/Mi/PzmN7+Zbf/ud79Lc+fObVN7DiJAgAABAgQIECBAgAABAgQIECBAYOkL1CYAveeeezK9HXbYIa2wwgrNJONW+K233jq9//772a3rzXYWvJg5c2b2HNEePXqkY489Nrv9vfGwbbbZJh166KHp29/+dpo9e3bjLtsECBAgQIAAAQIECBAgQIAAAQIECCxDArUJQON5nVFixmZRiQA0yhNPPFG0u1ndc889l+IW+M0337xp5mfjAV27dk1f/OIXs4WVWoatjcfZJkCAAAECBAgQIECAAAECBAgQIECgWoHaLIIUK7RHGTBgQKFoXp8vkFR40D8q33777Wxro402SvPnz0+33nprtqr8s88+m9ZZZ50sZN1zzz1TBKELK7ES/V133bWwQ9L06dOzsHWhB9lJgACBfwg0zjqP2erz5s1jQ4AAAQIElluBxsdRTZs2bYE7t5ZbGBdOgMAiBeIO0fj3vkKAwPIhUJsA9L333stGLA86Ww7fSiutlFXlx7Xc3/j6zTffzF727ds3xcJKd9xxR/aXqQg8J06cmK0CH7fcn3nmmalPnz6NpzbbfvLJJ9Oll17arC5/seaaa2abEWC0pU/5eb4TIEAgF4gwtDEQzet9J0CAAAECy6NATCxQCBAg0FaBOXPmmEzQVizHEaiBwMKnMHaSC4wZUBEkRllxxRULe92vX7+sftasWYX7Gyvfeuut7OW1116b7r///vTd73433XbbbVkQ+oMf/CCtssoq6dFHH00//elPG0+zTYAAAQIECBAgQIAAAQIECBAgQIDAMiZQixmgMTMzVn+PVdtbCzjz+p49ey5yCPIZVfG/yLEI0qhRo5rOiQWQTj755Gxl+Ouvvz7tvffeaY011mja37ix0047pVg1vqjEM0a/973vpQhm8xXmi45TR4AAgUaB+FmWz3CJn3ueQ9yoY5sAAQIEljeBqVOnppjFFaV///6LfETV8ubjegkQaF2gV69efma0zmMPgdoJ1CIAjVFZddVVUzzfM/4SVFTy+ritfVFltdVWyw6J2aSN4Wd+3lZbbZXi+aDPP/989tVaALruuuum+CoqcSt9lAhkBRhFQuoIECgSaHzmZ/fu3f38KEJSR4AAAQLLjUA89zMvEWZ069Ytf+k7AQIEFioQPy+6dOmy0GPsJECgPgK1uAU+hiMC0Ch50Jm9aPglZlxGactsyzwAzZ/T2dBM0+baa6+dbb/xxhtNdTYIECBAgAABAgQIECBAgAABAgQIEFi2BGoTgK6++uqZ7IsvvlgonNdvuummhfsbK/O2YkZp48qSjce8++672csNN9ywsdo2AQIECBAgQIAAAQIECBAgQIAAAQLLkEBtAtAdd9wxY73zzjsX4I1bRu+6666sftiwYQvsb1mxxRZbpAhB45miTz31VMvd2SzTuP09psy3JVBdoAEVBAgQIECAAAECBAgQIECAAAECBAgsFYHaBKAjR45MgwcPTs8991waO3ZsM7yrrroqvfPOO9mCRCNGjGi2749//GO2uvtLL73UVN+jR4+0zz77ZK9POumk7Nx8Z8wIvfDCC9N7772Xoi3P78xlfCdAgAABAgQIECBAgAABAgQIECCw7AnUZhGkeHjxAQcckI4//vh0+umnp3HjxqWhQ4emJ598MtuOUPOoo45a4CHH5513Xnr99dezczfYYIOmEfrsZz+b7rnnnvTYY4+lr33ta+njH/94imeCPvDAA2n8+PFpyJAhafTo0U3H2yBAgAABAgQIECBAgAABAgQIECBAYNkTqE0AGrTbb799Ouecc7IA9O67707xFSVmhh5++OEpVm9va4lVJM8999x06aWXphtuuCHdcsst2amxMvwOO+yQDjvssNSvX7+2Nuc4AgQIECBAgAABAgQIECBAgAABAgQqEKhVABp+w4cPT9dee21223osYhTP8hw0aFDq2rX4bv9rrrmmVfZ4xuc3vvGNbHboa6+9lt32HrNKW2ur1YbsIECAAAECBAgQIECAAAECBAgQIECgEoHaBaC54iqrrJLiqyNK3F6/zjrrdERT2iBAgAABAgQIECBAgAABAgQIECBAYCkKFE+LXIod8FYECBAgQIAAAQIECBAgQIAAAQIECBAoS0AAWpasdgkQIECAAAECBAgQIECAAAECBAgQqFxAAFr5EOgAAQIECBAgQIAAAQIECBAgQIAAAQJlCQhAy5LVLgECBAgQIECAAAECBAgQIECAAAEClQsIQCsfAh0gQIAAAQIECBAgQIAAAQIECBAgQKAsAQFoWbLaJUCAAAECBAgQIECAAAECBAgQIECgcgEBaOVDoAMECBAgQIAAAQIECBAgQIAAAQIECJQlIAAtS1a7BAgQIECAAAECBAgQIECAAAECBAhULiAArXwIdIAAAQIECBAgQIAAAQIECBAgQIAAgbIEBKBlyWqXAAECBAgQIECAAAECBAgQIECAAIHKBQSglQ+BDhAgQIAAAQIECBAgQIAAAQIECBAgUJaAALQsWe0SIECAAAECBAgQIECAAAECBAgQIFC5gAC08iHQAQIECBAgQIAAAQIECBAgQIAAAQIEyhIQgJYlq10CBAgQIECAAAECBAgQIECAAAECBCoXEIBWPgQ6QIAAAQIECBAgQIAAAQIECBAgQIBAWQIC0LJktUuAAAECBAgQIECAAAECBAgQIECAQOUCAtDKh0AHCBAgQIAAAQIECBAgQIAAAQIECBAoS0AAWpasdgkQIECAAAECBAgQIECAAAECBAgQqFxAAFr5EOgAAQIECBAgQIAAAQIECBAgQIAAAQJlCQhAy5LVLgECBAgQIECAAAECBAgQIECAAAEClQsIQCsfAh0gQIAAAQIECBAgQIAAAQIECBAgQKAsAQFoWbLaJUCAAAECBAgQIECAAAECBAgQIECgcgEBaOVDoAMECBAgQIAAAQIECBAgQIAAAQIECJQlIAAtS1a7BAgQIECAAAECBAgQIECAAAECBAhULiAArXwIdIAAAQIECBAgQIAAAQIECBAgQIAAgbIEBKBlyWqXAAECBAgQIECAAAECBAgQIECAAIHKBQSglQ+BDhAgQIAAAQIECBAgQIAAAQIECBAgUJaAALQsWe0SIECAAAECBAgQIECAAAECBAgQIFC5gAC08iHQAQIECBAgQIAAAQIECBAgQIAAAQIEyhIQgJYlq10CBAgQIECAAAECBAgQIECAAAECBCoXEIBWPgQ6QIAAAQIECBAgQIAAAQIECBAgQIBAWQIC0LJktUuAAAECBAgQIECAAAECBAgQIECAQOUCAtDKh0AHCBAgQIAAAQIECBAgQIAAAQIECBAoS0AAWpasdgkQIECAAAECBAgQIECAAAECBAgQqFxAAFr5EOgAAQIECBAgQIAAAQIECBAgQIAAAQJlCQhAy5LVLgECBAgQIECAAAECBAgQIECAAAEClQsIQCsfAh0gQIAAAQIECBAgQIAAAQIECBAgQKAsAQFoWbLaJUCAAAECBAgQIECAAAECBAgQIECgcgEBaOVDoAMECBAgQIAAAQIECBAgQIAAAQIECJQlIAAtS1a7BAgQIECAAAECBAgQIECAAAECBAhULiAArXwIdIAAAQIECBAgQIAAAQIECBAgQIAAgbIEBKBlyWqXAAECBAgQIECAAAECBAgQIECAAIHKBQSglQ+BDhAgQIAAAQIECBAgQIAAAQIECBAgUJaAALQsWe0SIECAAAECBAgQIECAAAECBAgQIFC5gAC08iHQAQIECBAgQIAAAQIECBAgQIAAAQIEyhIQgJYlq10CBAgQIECAAAECBAgQIECAAAECBCoXEIBWPgQ6QIAAAQIECBAgQIAAAQIECBAgQIBAWQIC0LJktUuAAAECBAgQIECAAAECBAgQIECAQOUCAtDKh0AHCBAgQIAAAQIECBAgQIAAAQIECBAoS0AAWpasdgkQIECAAAECBAgQIECAAAECBAgQqFxAAFr5EOgAAQIECBAgQIAAAQIECBAgQIAAAQJlCQhAy5LVLgECBAgQIECAAAECBAgQIECAAAEClQsIQCsfAh0gQIAAAQIECBAgQIAAAQIECBAgQKAsAQFoWbLaJUCAAAECBAgQIECAAAECBAgQIECgcgEBaOVDoAMECBAgQIAAAQIECBAgQIAAAQIECJQlIAAtS1a7BAgQIECAAAECBAgQIECAAAECBAhULiAArXwIdIAAAQIECBAgQIAAAQIECBAgQIAAgbIEBKBlyWqXAAECBAgQIECAAAECBAgQIECAAIHKBQSglQ+BDhAgQIAAAQIECBAgQIAAAQIECBAgUJaAALQsWe0SIECAAAECBAgQIECAAAECBAgQIFC5gAC08iHQAQIECBAgQIAAAQIECBAgQIAAAQIEyhIQgJYlq10CBAgQIECAAAECBAgQIECAAAECBCoXEIBWPgQ6QIAAAQIECBAgQIAAAQIECBAgQIBAWQIC0LJktUuAAAECBAgQIECAAAECBAgQIECAQOUCAtDKh0AHCBAgQIAAAQIECBAgQIAAAQIECBAoS0AAWpasdgkQIECAAAECBAgQIECAAAECBAgQqFxAAFr5EOgAAQIECBAgQIAAAQIECBAgQIAAAQJlCQhAy5LVLgECBAgQIECAAAECBAgQIECAAAEClQsIQCsfAh0gQIAAAQIECBAgQIAAAQIECBAgQKAsAQFoWbLaJUCAAAECBAgQIECAAAECBAgQIECgcgEBaOVDoAMECBAgQIAAAQIECBAgQIAAAQIECJQlIAAtS1a7BAgQIECAAAECBAgQIECAAAECBAhULiAArXwIdIAAAQIECBAgQIAAAQIECBAgQIAAgbIEBKBlyWqXAAECBAgQIECAAAECBAgQIECAAIHKBQSglQ+BDhAgQIAAAQIECBAgQIAAAQIECBAgUJaAALQsWe0SIECAAAECBAgQIECAAAECBAgQIFC5gAC08iHQAQIECBAgQIAAAQIECBAgQIAAAQIEyhIQgJYlq10CBAgQIECAAAECBAgQIECAAAECBCoXEIBWPgQ6QIAAAQIECBAgQIAAAQIECBAgQIBAWQIC0LJktUuAAAECBAgQIECAAAECBAgQIECAQOUC3SvvwXLagfnz52dX/v7776eZM2cupwoumwCBxRWYPXt20ylz5szx86NJwwYBAgQILI8C+d+p49pnzZqVunY1v2N5/By4ZgLtEZg7d25q/BnSnjacQ4BA5xEQgFY8VtOmTUuTJk2quBfengCBzigwY8aMFF8KAQIECBAgkNLkyZMxECBAoM0C8Z8m8+bNa/PxDiRAoHMLCEArGr8uXbpk7zxgwIC0+uqrV9QLb0uAQGcTiMBz6tSpWbf79u2b4kshQIAAAQLLq0BMJMjvjlh11VXNAF1ePwium0A7BHr37p26devWjjOdQoBAZxQQgFY8anGbjh+6FQ+CtyfQiQQab+3z86MTDZyuEiBAgEDpAv5cLJ3YGxColUA+KalWF+ViCBBoVcBDclqlsYMAAQIECBAgQIAAAQIECBAgQIAAgc4uIADt7COo/wQIECBAgAABAgQIECBAgAABAgQItCogAG2Vxg4CBAgQIECAAAECBAgQIECAAAECBDq7gAC0s4+g/hMgQIAAAQIECBAgQIAAAQIECBAg0KqAALRVGjsIECBAgAABAgQIECBAgAABAgQIEOjsAgLQzj6C+k+AAAECBAgQIECAAAECBAgQIECAQKsCAtBWaewgQIAAAQIECBAgQIAAAQIECBAgQKCzCwhAO/sI6j8BAgQIECBAgAABAgQIECBAgAABAq0KCEBbpbGDAAECBAgQIECAAAECBAgQIECAAIHOLiAA7ewjqP8ECBAgQIAAAQIECBAgQIAAAQIECLQqIABtlcYOAgQIECBAgAABAgQIECBAgAABAgQ6u4AAtLOPoP4TIECAAAECBAgQIECAAAECBAgQINCqgAC0VRo7CBAgQIAAAQIECBAgQIAAAQIECBDo7AIC0M4+gvpPgAABAgQIECBAgAABAgQIECBAgECrAgLQVmnsIECAAAECBAgQIECAAAECBAgQIECgswsIQDv7COo/AQIECBAgQIAAAQIECBAgQIAAAQKtCghAW6WxgwABAgQIECBAgAABAgQIECBAgACBzi4gAO3sI6j/BAgQIECAAAECBAgQIECAAAECBAi0KiAAbZXGDgIECBAgQIAAAQIECBAgQIAAAQIEOruAALSzj6D+EyBAgAABAgQIECBAgAABAgQIECDQqoAAtFUaOwgQIECAAAECBAgQIECAAAECBAgQ6OwCAtDOPoL6T4AAAQIECBAgQIAAAQIECBAgQIBAqwIC0FZp7CBAgAABAgQIECBAgAABAgQIECBAoLMLCEA7+wjqPwECBAgQIECAAAECBAgQIECAAAECrQoIQFulsYMAAQIECBAgQIAAAQIECBAgQIAAgc4uIADt7COo/wQIECBAgAABAgQIECBAgAABAgQItCrQvdU9HbDjzTffTM8880yaPn16mjNnziJb/MAHPpCGDh26yOMcQIAAAQIECBAgQIAAAQIECBAgQIAAgbYIlBKARuh53HHHpd/85jdtCj7zjp5wwgnpxBNPzF/6ToAAAQIECBAgQIAAAQIECBAg0CBw//33p+22266hpvlmly5dUo8ePVLv3r3TWmutlXbddde03377pa222qr5gRW9euCBB9I222xT0bt72+VVoMNvgZ86dWraY4890pgxYxYr/FxeB8B1EyBAgAABAgQIECBAgAABAgQ6SmD+/Pnp/fffT5MnT04TJkxI5557bvrgBz+Y9txzzzR37tyOepvFbudvf/tb+spXvpJ23HHHxT7XCQSWVKDDZ4CefPLJ6dlnn836Fbez77333mnw4MFp0KBBKf4XYmFlo402Wthu+wgQIECAAAECBAgQIECAAAECBP4hEDNBR40atYDH7Nmz03vvvZf+/Oc/p9///vfZ/htvvDEdeeSR6Zxzzlng+KVREXf8XnnllalXr15L4+28B4FmAh0egMZU7CjDhw9Pd955Z1p55ZWbvaEXBAgQIECAAAECBAgQIECAAAECSy4wcuTIdMwxxyy0oZ///OfpoIMOStOmTctmg/7Lv/xL+shHPrLQc+wkUDeBDr0FPqZSP/7445nR17/+deFn3T4trocAAQIECBAgQIAAAQIECBDoVAL77rtv2n///Zv6nE9ca6qwQWA5EOjQGaDdunVLffv2TTNmzMhmgC4Hfi6RAAECBAgQIECAAAECBAgQILBMC+y2227pvPPOy/o4bty4dNhhhxX297XXXktPP/109vXGG29kjzSMxxtuttlmadVVVy085+WXX05vvvlm6t+/f9p4442z7d/+9rfZ91js6AMf+EB69dVXs9fRQDyj9MEHH8zaiscldu/ePdsfFXF+tNNaidv6n3rqqWz3kCFD0iqrrNLaoeoJNBPo0AA0Wv7Yxz6WbrrpphS/AT7+8Y83ezMvCBAgQIAAAQIECBAgQIAAAQIElq7Ahhtu2PSGU6ZMadrONyZOnJjiTt78eaF5ff59hRVWSCeccEL2DNEILBvLmWeemX7yk5+kXXbZJZ1yyilpp512SrFAdl5i0aPGdmOBphEjRmS7I4iNhbQ/9alPZa+PP/74dNJJJ+WnLvD90ksvTYceemhWH+vPCEAXIFLRikCH3gIf75F/aC+55JJW3lI1AQIECBAgQIAAAQIECBAgQIDA0hLIH1cY77fFFls0e9s77rgjq4uQsmvXrikWVorb5uNZoR/96EezY2fOnJmOPvroLARtdnLDi7gbeJ999mkWfsbueE5pPHN0tdVWy46OBbLjdXytu+666ROf+EQ20zR2XnXVVdkxrf0SzzONEhPuYmaqQqCtAh0egB5yyCHp85//fLrnnnvSt7/97QU++G3tmOMIECBAgAABAgQIECBAgAABAgSWTCDCy9GjRzc1EoFkYzn22GOz7CZmUz722GPp3nvvTVdccUW2Ynvcqv7HP/4xrbTSStkpF110UZo+fXrj6U3b9913X3rhhRey4PQPf/hDuuWWW9Jxxx2XTj311PTQQw+lL3zhC9mxPXv2zF5H3RFHHJEiEP3qV7+a7Yvz4xb9ojJ+/Pj0yCOPZLu+9rWvFR2ijkCrAs3nLbd6WNt3xLMYDjjggOx5DhdccEG6+uqr04c//OG0/vrrN6X9rbUWs0c/+clPtrZbPQECBAgQIECAAAECBAgQIECAwD8E5s2bl2JB6pZl8uTJ2XM1I8D8wQ9+kJ5//vnskJg5GZPW8hJhYwSRUU4//fS05ZZb5ruavsdzPGMV+TPOOCNNmjQpxa3nw4YNa9rfuLHtttummKUZM0mjfOYzn2nc3ep2BJonn3xy9nzQK6+8Mnu8YsuDf/GLX2RVvXv3TnvttVfL3V4TWKhAhwegMSX61ltvbXrTt956K912221Nrxe2Ec+REIAuTMg+AgQIECBAgAABAgQIECBAgMD/Cvz4xz9O8dWWsuaaa2bP6owZl3mJBYruvPPObObm3nvvnVcv8D0WJ8rLtGnT8s0FvkdQmoefC+xcSMXgwYOzW+Hvvvvu9Otf/zqde+65qUePHk1nRNAbwWiUz33uc00zUpsOsEFgEQIdfomd+hgAAEAASURBVAv8It7PbgIECBAgQIAAAQIECBAgQIAAgaUk0KtXr/T9738/m7m5+eabN3vXuO09Fin6xje+kVZcccVm+2JmadySHsHjr371q6Z9c+bMadpuubEkz+Xcb7/9subeeeedBSbSRTAaK8lHcft7xuCXxRTo8BmgkdQv7DfDwvoXq4opBAgQIECAAAECBAgQIECAAAECixbYfvvt02677bbAgSuvvHL2KMKYWbneeuulCEEXVSJkjPVc4tGGEyZMyG6bjxXbF6dstNFGi3N4s2PjGaEHH3xwilXqI3Tdfffdm/bnix+ts846WWDbtMMGgTYKdHgA2q9fvza+tcMIECBAgAABAgQIECBAgAABAgTaKzBixIh01FFHtff07Lwnnngim1X56KOPLtBO3759U4Ss/fv3z9Z4WeCAhop4rGEc197Sp0+f7Nmel1xySbrpppuyIDQWX3rvvffS9ddfnzX7la98pV232Le3T86rj8BSuwU+nhHx+OOPp6effjr9/e9/zx5sWx9GV0KAAAECBAgQIECAAAECBAgQ6FwCL7/8cho1alTKw89YJOn4449PY8aMyW6Zj9mYsc5LHLM0Sn4b/IwZM5pCzxtuuCHlzx11+/vSGIV6vkeHzwBtZIpp08cee2x65JFHmp7VkO+P50zEg2u/+c1vZqvE5/W+EyBAgAABAgQIECBAgAABAgQIlC9wzjnnpNdffz17o1hM6Ygjjih805jIlpeiVefzfUv6PVacjwWXnnnmmXTddddlM1MjAI3ysY99LMWiTQqB9giUMgM0ngH6ve99Lw0fPjzdeOONC4Sf0dF4qG1Max45cmQ666yz2tN35xAgQIAAAQIECBAgQIAAAQIECLRTIJ75GSUmqR122GHZdtEvf/rTn5qq27vuS776/Pz585vaKtrIZ3nG6vSRHd1+++3ZYXl90TnqCCxKoJQZoKeddlpTqBkf8E984hNZgr/++uun6dOnp7/+9a/pscceS/GciTwsHTRoUNp3330X1V/7CRAgQIAAAQIECBAgQIAAAQIEOkAgX+Ro6tSp6W9/+1tac801F2g1FiS69tprm+pnz57dtL04G/nC1/GecUt7a2vIRDZ03HHHpZkzZ2bPN41ngMa5X/rSlxbn7RxLoJlAhweg8dyICECjxNTlCy+8MJsJ2uxd//EiHmp76KGHppdeeikddNBBaY899kgDBgwoOlQdAQIECBAgQIAAAQIECBAgQIBABwrEIkqxVkuEksccc0w644wzUkxQi/L2229nwechhxzSbB2XSZMmtasHAwcObDrvpJNOyjKgmHm62WabNdXHxlprrZV22WWXNHbs2HT55Zdn+/bcc88lWmCp2Rt4sVwKdPgt8Oeff36K/w3YYIMN0i233NJq+Bnau+++e7ayV6z0Fen/VVddtVwOgosmQIAAAQIECBAgQIAAAQIECCxtgVNPPTWtvPLK2dv+7Gc/y8LHrbfeOm2xxRZp9dVXTwceeGBaddVVs0cYxirvUf7yl79k3xf3lx122CHlt8H/6Ec/ylaXP/roowubyRdDym+Xd/t7IZPKxRDo8AA0bmuPcsIJJ7RpNufmm2+e9t9//+ycu+++O/vuFwIECBAgQIAAAQIECBAgQIAAgXIF1llnnfSHP/wh7bTTTtkbReD40EMPpVjUesUVV8wWro4ZopHbxBouUa6++upmM0Kzyjb8sv3222d3CccMz7xMmDAh32z2Pe4QzoPZtddeu6l/zQ7ygsBiCHToLfCxEtj48eOzt4//MWhryY+NZ4MqBAgQIECAAAECBAgQIECAAAECxQLbbrttuwLI4tZSNtvzjjvuyFaDf+GFF9KUKVNSTFZbb731mmZsxrn33XdfYRMXX3xxiq+2lJhRGl+x8nysERMBbFHp1q1bymecxjNB47VCYEkEOjQA7dq1a9MHNB5S29YSH/oo/fv3b+spjiNAgAABAgQIECBAgAABAgQIEOgggVgAqWgRpA5qvlkzi3qfeP7nm2++mQWwX//615ud6wWB9gh06C3w8SyHTTbZJOvH/fff3+b+5P+LsOWWW7b5HAcSIECAAAECBAgQIECAAAECBAjUSyDWlTnrrLOyi9pxxx3TRhttVK8LdDWVCHToDNC4glhB7OGHH04nnnhitqLXhhtuuNALu/3225sWPxo+fPhCj7WTAAECBAgQIECAAAECBAgQIECgXgIPPPBAevzxx7PV6MeMGZPySXWtLZJUr6t3NUtDoENngEaHjz322NSvX780efLkFCt8XXbZZSmeDdqyTJ06NZ188snpi1/8Yvbsipj9+eUvf7nlYV4TIECAAAECBAgQIECAAAECBAjUWOCVV17Jng162GGHNYWfo0ePTp/61KdqfNUubWkKdPgM0HiOwxlnnJEOOeSQ9Oqrr2YrhR155JEpZoIOHjw4S/NffvnlFA/WzZ/92aNHj3TFFVeknj17Ls1r914ECBAgQIAAAQIECBAgQIAAAQIVC8Rt7r169UqzZs1K6667btpnn33SSSedVHGvvH2dBDo8AA2cgw8+OG288cYpHlQbIeikSZPSI488kn21xBs2bFi68MILk9vfW8p4TYAAAQIECBAgQIAAAQIECBCov8CHP/zhNG3atPTGG2+0ujJ8/RVcYZkCpQSg0eGdd945/eUvf0kXXXRR9n3ChAnp6aefTjHbc+jQodlXTGXeb7/9Urdu3cq8Rm0TIECAAAECBAgQIECAAAECBAgswwLdu3cXfi7D49PZu1ZaABow/fv3T9///vebjObPn59ipXiFAAECBAgQIECAAAECBAgQIECAAAECS0OgwxdBWlinhZ8L07GPAAECBAgQIECAAAECBAgQIECAAIGOFliqAWhHd157BAgQIECAAAECBAgQIECAAAECBAgQWJhAu2+Bv+uuu9Kdd96Ztb3rrrumHXbYIduO1dyfeeaZhb1nq/t23HHHFF8KAQIECBAgQIAAAQIECBAgQIAAAQIEOkKg3QHofffdl84444ysD/GszzwAveaaa9Ktt97arr716tVLANouOScRIECAAAECBAgQIECAAAECBAgQIFAk4Bb4IhV1BAgQIECAAAECBAgQIECAAAECBAjUQqDdM0CPPvrodMQRR2QIMXMzL2PGjElz5szJXy7W98Z2FutEBxMgQIAAAQIECBAgQIAAAQIECBAgQKBAoN0BaM+ePVN8tSy9e/duWeU1AQIECBAgQIAAAQIECBAgQIAAAQIEKhHo8Fvgf/rTn6bDDjssjR8/vs0XdMopp6Rtttmm6ZmibT7RgQQIECBAgAABAgQIECBAgAABAgQIEFiIQLtngLbW5o033pgtgrTzzjunzTbbrLXDmtXfc889ady4cWnIkCHN6r0gQIAAAQIECBAgQIAAAQIECBBIadq0aWnAgAGdjuL0009PRx11VKfrtw7XS6DDA9DF4Zk7d2567rnn0uOPP56d1qdPn8U53bEECBAgQIAAAQIECBAgQIAAgeVGIHKULv0HpO7rrr/MX/P896alOS+9kObNm7fM91UH6y+wRAHobrvtln7/+983U5o9e3b2+nOf+1zq2nXhd9jHsY2/ET7ykY80a8sLAgQIECBAgAABAgQIECBAgACB/xPosfHmaaWD/ndR6v+rXfa2Zj87IU0+7bhlr2N6tFwKLFEA+uMf/zhttdVWKQ89GwWL6hr3t9zeYost0p577tmy2msCBAgQIECAAAECBAgQIECAAAECBAi0W2CJAtBNNtkkXXTRRemhhx5q6sDYsWPTxIkT06c//em03nrrNdUXbfTo0SP17ds3bbDBBmmvvfZKAwcOLDpMHQECBAgQIECAAAECBAgQIECAAAECBNolsEQBaLzj/vvvn33l7z5q1KgsAD3ooINS3CKvECBAgAABAgQIECBAgAABAgQIECBAoCqBhT+ksx296tmzZ1pppZXSTTfdlKZPn96OFpxCgAABAgQIECBAgAABAgQIECBAgACBjhHo8AD0kUceSVOmTEljxoxJvXr16pheaoUAAQIECBAgQIAAAQIECBAgQIAAAQLtEOjQAHTu3Lnprbfeyrqx7bbbpm7durWjS04hQIAAAQIECBAgQIAAAQIECBAgQIBAxwh0aAAagef222+f9ezBBx9M8+bN65heaoUAAQIECBAgQIAAAQIECBAgQIAAAQLtEOjQADTe/9RTT82eAfr666+nf/u3f8tuh29Hv5xCgAABAgQIECBAgAABAgQIECBAgACBJRZY4lXgW/ZgwIAB6eKLL05HHXVUuvzyy9MNN9yQNt100zR06NA0ePDgFIsktVZi9uh2223X2m71BAgQIECAAAECBAgQIECAAAECBAgQWCyBDg9Av/Od76Rbb721qRPvvvtuGjduXPbVVNnKxoknnigAbcVGNQECBAgQIECAAAECBAgQIECAAAECiy/Q4bfAL34XnEGAAAECBAgQIECAAAECBAgQIECAAIFyBDp8BuiVV16ZZs2a1a7e9uvXr13nOYkAAQIECBAgQIAAAQIECBAgQIAAAQJFAh0egA4cOLDofdQRIECAAAECBAgQIECAAAECBAgQWCyBZ599Nu2zzz4LnNOlS5fUq1ev1L9//7T55punb33rW2mDDTZY4DgVSy4wd+7cNHHixE7t2+EB6JKzaoEAAQIECBAgQIAAAQIECBAgQIBAStOnT0+PPPLIQiliLZpzzjkn/eu//ms699xzs1B0oSfY2WaBhx9+OO2///7py1/+cjr66KPbfN6yduBSC0CnTZuWXnjhhSydX3311VPMFI20vqNL3H4/ZsyYFAM0adKkbPX5YcOGpVGjRqVu3bot0dvNnz8/nXzyyemVV15Jp512WlpjjTWWqD0nEyBAgAABAgQIECBAgAABAgQItE3gv//7v7NcKY6OjOa9995Lb7zxRrr99tvTZZddln72s5+l2bNnp3g8o9IxAldccUV64oknsgC0Y1qsppVSA9CnnnoqHXvssVlS/+qrrza7wlVWWSV97nOfS9/85jfThz/84Wb72vsiVpw/8MADs4Ay2lh55ZXTbbfdln098MAD6YQTTkg9e/Zsb/Pp17/+dbrzzjuz899///12t+NEAgQIECBAgAABAgQIECBAgACBxRPYaqut0gorrLDASV/4whfS3nvvnU1+u+qqq9I//dM/pX/+539e4DgVy69AKavAz5kzJ33ve99Lw4cPTzfeeGNqGX4G9zvvvJMuueSSNHLkyHTWWWd1yAiccsopWfg5YsSIdPPNN2fvffXVV6chQ4ake++9N51//vntfp/nn38+/eQnP2n3+U4kQIAAAQIECBAgQIAAAQIECBAoR+CTn/xkOumkk7LGY7Jd3DpfVGLy3D333JP+67/+K/3+97/P8qmi4yK3iol9cXyUyZMnZ1nTtddem958881mp8TdyH/4wx+ynCu+z5s3r9n+xhcxQzVu6f/FL36RfvWrX2XvETnaokr057rrrks///nP05/+9KdsBmzLc8aPH5/imalRXn755ew9XnzxxZaHpZkzZ2azOuNaIpu7++670+uvv97suHjuZ1z/3//+96z+b3/7W/Y6vnfGUsoM0Lg9PA814zb3T3ziE2njjTdO66+/fvYB/Otf/5oee+yxDDsPSwcNGpT23XffdhvGID/44IOpd+/e6dRTT236H4G11147nX322enzn/98Gjt2bPr3f//3tOKKKy7W+8QHOW597969e+ratWsy+3Ox+BxMgAABAgQIECBAgAABAgQIEChd4IADDsju/o3QbsKECc3uOI5A7/TTT8/yncbAMR6XGHcvjx49Ost98k7G7fRHHnlkFpTmj0KMNvJy8MEHpwsuuCC76zhmm8ajH/Oy9dZbZ2Hpaqutlldl3yOXOuyww5pCynznJptskuJW8zivZXn88cfTV7/61SxDi9v+8/KhD30ou+3/gx/8YF6VTUSMfC2egxp9yvv74x//OB1xxBHZcZdeemm2PWXKlKbzYiPyrrirOvK8yNYicN1iiy2ajjnvvPNSfH3nO99JP/rRj5rqO8tGhwegjz76aPZ8zADYZptt0oUXXpgNQBHITTfdlA499ND00ksvpYMOOijtscceacCAAUWHLrIu0vsoO+ywQ1P4mVX8zy9xK3x8iMaNG5eFoHvttVe+q03fL7744qyP3/3ud7MPvgC0TWwOIkCAAAECBAgQIECAAAECBAgsNYF43GIEgzFDMibKNT5yMbKg66+/PsW6NBHibbrppum5557L7haOSW8x2zHWlGlZIuyLu4LjNvttt9226VGLkXfFbM7LL788feYzn0m77rprtlL6f/7nf2YT9CJQjTwpL5Fb7bbbbtnMzS996Uvps5/9bDZT9De/+U264YYbsgwtHuO400475aekmED46U9/OpudGYFm5GZTp05tmgUa7xuzSSP0zEvsjyA4HhUQa+LE/mgjSlxnPB5y1VVXzSYIxvW89dZbWVgbd07HNcXkxQh+Y/JgBKkx6/S+++7L+r7zzjtnvvl7dabvHX4LfNxmHh+ADTbYIN1yyy2thp+BtPvuu6cIQfv06ZMl5fGchvaW+KBGidvfi0qeoseDWxenPPTQQ9lvgI997GPZB21xznUsAQIECBAgQIAAAQIECBAgQIDA0hPIw8AIQPMSwWKEn7EvcqGjjjoqy6RiVmQEhIMHD86Cvt/97nf5KU3fn3nmmXTmmWdm68IccsghWda13377ZfvjUYmHH354FmDGbfcxwzRuKY8Ssz3zEqFknBMzOGM2ZjyuMVasjzuho18nnnhiNlszZmDGXch5iYAzbk2P0PKaa67JzvnWt76V7r///izA/X//7/8t8LjGWBA8wsvXXnstOy5eR9gbt71HwBklQtoIZ6MP0f94FEDcMR0lX0AqZoHGpMV8hunHP/7x7PV2222XHdfZfunwGaB5wBiD05bZnJtvvnnaf//9s2nD8cyBmAnanhIDG6W198zrY9pyW0s83yFu5+/fv3/6/ve/39bTmo6LacqRkheV/HkQM2bMyNL7omPUESBAoKVA/AdTXuIPxsZbIPJ63wkQIECAwPIikN/aF9cbKwHH47cUAgQItEUg/l7t79Jtkep8x+T5T+OzL88444zsQiJoXGONNZpdVMwa3WeffbLwMh6huMsuuzTbv+aaa2YzIhsrt99++xS3ksefO3H7fGOJtW6ixHo4cat9PE4xAth4Jmfc6h6hYstyzDHHZLfAx4zUmCj4xS9+MZtNGiFu9DfW2Wkscdt+3I4ei3VvtNFGjbuy7ciwIsuKki8aFX9OxjNS45mmRQtExULl//Ef/9H0zM/s5Br90qEBaPwFJE/Y8xmXbbHKj42pve0tMZBR8g96y3ZWWmmlrCo/ruX+otc//OEPs2ceRAgat9Evbvnzn/+cBbtF58VvoCgRgDY+J6LoWHUECBAoEojHcXgkR5GMOgIECBBYHgUW5+/5y6OPayZAoLlABKD5xKTme7zq7AL5Qtz5TNC4njyriuAwf4Ri43XmGc3TTz/dWJ1tx8LaLcvQoUOzqjiv5TozecAan6/4nEUAGs8jjRK3t0cfWpYePXqkWMQpHhGZHxtr50T56Ec/mj2Ts+U5cTt6fBWVCFpblgh6Y/ZoY4nZobFo0pNPPpndBh/7Gp+P2nhsZ9/u0AA0HpgaAxtlcf4Ckq/MlafTi4saH6qYyhul5Qcvb6tfv37ZZuNU4nxf0fe4fT9W7ornJESyrxAgQIAAAQIECBAgQIAAAQIECCzbAi+88ELWwQ033DD7HjMe33777Ww7no25sBJ3DUdu1KtXr6bD4vb4liW/46Aox4psLC/5cXmoWdRWfmw8SjJKzAKNkgeg6623XvZ6cX7Jr73lOTFxMZ7pGbNX4w7uN954o+mQPM9rqqjZRocGoDGwkTI//PDD2XMGPvKRj7SJK79NfMstt2zT8S0Pig9XPJsgZlO2FnDm9T179mx5+gKv4xkKMZU4/regaGryAie0UhELMrVc8Ss/NALi448/PkUw29qs1fxY3wkQIJALxM+y+FkXJW5lyG9nyPf7ToAAAQIElieBeKZafht83PHV+I/O5cnBtRIgsPgCkQ34mbH4bsv6GfFvpYkTJ2bdzEPAxkcdxLM825ILNV5n0b+5GttsPLbldn5czPCM0vhIs5bH5v/Oy4/N/3xb2Dkt28hfNwa4eV30JZ45+stf/jKrWmeddbJb4eMZn5HfxZ+jsZh5XUuHBqCBFIsQRQB64oknZosG5R+41gBvv/32lC9+NHz48NYOW2R9rGAVSX38Jaio5PV9+/Yt2t1UFx+wWBUrZpSeddZZaVHHN51YsBHX3tr1578h4zdehLcKAQIE2iIQf2g1/sHo50db1BxDgAABAnUViEkF+T8Q4x+oRbcV1vXaXRcBAksmELPd8tl5S9aSs5clgVixPf5ciNu9P/WpT2VdGzhwYLbqecwCjed7Lkn21N5rzW+Zj+eAtlbyx0LGKvVR8md75rf0tzwvcqtYsChu0Y/b5xdVYqX5CD9j1upvf/vbBe52jsWYotT10RD/Ny93UVJt3B8Pf41ZjbGAUMyAvOyyy5r+UtLYRASSETTGg13jH/Qx+/PLX/5y4yGLtR0BaJQ86Gx58pQpU7Kq+OAvrDz//PMpVpSPH4SjR4/OVgWL1erzr5g6HSVW94q6uFVeIUCAAAECBAgQIECAAAECBAgQqE4gwsWY4Rnl4IMPbjahbYst/j979wKvVVXnD3hxR0AQUfMSiRqCWgRZXlNMx7FGx6yhUiczzdvkaFqO2r+833XSNMxKx8bSMnVS08S8j+a1vCSImXlJ8BITinITOMDf37b3dN7zvgfOgX04+93nWZ/P4bzvvqy91rNeOZ4va+/1gWx73P5dr8TK7bFS+v77719v90pvq1w/gsfKhJaWlUaGVlk1vvIYxs033zw75IEHHsgWLmp5fLy+9957U9zSHyvat6fEYx6j7LHHHjXhZ2x//PHH41vNM0ArM6Ur/+CYHdSAf+QegMYDYCura0VKHSu8x23gMZ02ws4999wzjRkzJru9PFaKjwWAYnrvFVdc0eFpyC29Kwl5y1W+Wu6vbI8P9PJK/MtxBKARprb+qpwb/9oc+1ZkKnKlDt8JECBAgAABAgQIECBAgAABAgRWTGDGjBnZXchxB+/YsWNTrDGz6aabpiOPPLKqwsifosQM0QgOW5aYefm1r30txQJII+o877PlsSv6+lOf+lQ28/TVV1/NVpRvGSZGrnTUUUdlq69vscUW2UJJcZ0tt9wy7bbbbtkEw1jVveXMzMikvvWtb2XN+fznP9+uZlUWB4/HULZe6OiWW25J55xzTlZPZY2dSqWVuw4rM1Qr2xvte+63wAdAJO2jRo1KBx54YIoQNFaVevTRR7Ov1kDxAZ04ceJKT0HeZZddUtxOf8cdd6R999236jLxIbnrrruybXG9ZZVo9z333NPmITHrM2aB/uQnP0nDhw9v8zg7CBAgQIAAAQIECBAgQIAAAQIE8hNoufB13E3cMkiMq8SsyciF1lxzzaqL7rTTTtmdvN///vez28Vjgl5MznvsscfSbbfdlk3Oi0c6tnc2ZVXl7XgTsyi/973vZbfgx/eHHnoofeITn8hCzQgfY0GiWFPnzjvvzCYJVqq84IIL0vbbb59+8IMfZDM0IxCNIDJuV48JhTvssEMWnlaOX9b3CErPO++87Bmp0de99torW+wpZobGdSMvi3ZEuBozUisLPMUt9lHiDu/f/va32cTGuKO70UqnBKCBsOuuu6YpU6akSy65JPseK15Fmh6zPePZB/EVz2M44IADcnlWzzbbbJMl9bFaVkwbjtXbKyWeMTpz5sy04YYbZs8orWyP7/fff3/2LwTxbIXKilst93tNgAABAgQIECBAgAABAgQIECDQ9QKtZy7GYw432GCDbP2VuH09Zlq29TzoyKciMDzuuOPSNddck31Fj2KGYyyAHbNEV2YdmOXpRG41efLkdOihh2aBY4SvUWIxov322y+bgfme97ynqpq4izmytUMOOSSb9PfII49k++MZttHmU045JbV39fa4DT8eAXDEEUdkwW/l+nFHdQSjhx9+eBbQRhgazwv90pe+lF0rvt9+++3pxhtvTL///e+zx14KQKuGKWVpcUzTrZRI5zvrIcNRbzz7IFZWP/PMM9ODDz6Yhazx4YrXEbxGkt/6+rHae0xBjnMFoJWR8p0AAQIECBAgQIAAAQIECBAg0PUCMTMx8qQ8StwxHF8xSe6ZZ55JEaBGFlRvpfevf/3rKb7qlW233bbNNkUA21Z7Y2LerbfemhYsWJBiomCsoVNZ7KjedWLb+uuvn26++ebsnKlTp6aYTRp3L9drc9S7rLL77rtnM09jFukrr7ySLaAUj7KslJg927pEnhaB8cKFC7P8rOXxrY8t8vtOmwFar9PxAYuBaj0Vud6xK7ItHhQb04MjAL377ruzr6gnnuFw9NFHZ9ObV6Re5xAgQIAAAQIECBAgQIAAAQIECJRDIFaJ32677bqsM/369ctuOe9IA+KcPFawj4B24403zr46cv2+fftmd1Z35JwiHdupAWhMzY0Vrp544onsWQWRLkcJtFgU6dOf/nS2SNLyVmbvCFh8GK699toszZ82bVqKqbzrrrtuFrzWqydS7I6Um266qSOHO5YAAQIECBAgQIAAAQIECBAgQIAAgS4U6JQANFaMimcaxEJB9ab9xrTZBx54IPuK5wxcfPHF2QrxeTpEmh9fCgECBAgQIECAAAECBAgQIECAAAEC3Vcg9wA0VuCKlaVi5meUuOW9skBRrJoeq1S99NJL2UNcX3jhhTRjxozs+FjBKh5WqxAgQIAAAQIECBAgQIAAAQIECBAgQCAvgdwD0LilvBJ+7rnnnunss89OsWpV67JkyZJ03XXXZatvvfjii9mKVxGMrrHGGq0P9Z4AAQIECBAgQIAAAQIECBAgQIAAAQIrJNBzhc5axkkTJ07M9u66664pZnXWCz/jgJgZ+rnPfS5NmjQpDR48OM2ePTv98Ic/XEbNdhEgQIAAAQIECBAgQIAAAQIECBAgQKBjArkHoFOmTMlacMYZZ6RYWWp5ZfTo0dnzQuO4++67b3mH20+AAAECBAgQIECAAAECBAgQIECAAIF2C+QagDY1NaVYAKlHjx5pzJgx7W5ErAgf5bXXXmv3OQ4kQIAAAQIECBAgQIAAAQIECBAgQIDA8gRyDUB79+6dxo0bl638XpkJurwGxP54BmiUsWPHZt/9QYAAAQIECBAgQIAAAQIECBAgQIAAgTwEcg1Ao0E77LBD1q64BX7p0qXLbeP8+fPTlVdemR03fvz45R7vAAIECBAgQIAAAQIECBAgQIAAAQIECLRXIPdV4E844YR05513puuvvz4ddNBB6ZxzzklrrbVW3fbMmDEjHXbYYWny5Mlpt912S/vss0/d42wkQIAAAQIECBAgQIAAAQIECBBIaen8ualp+kuFp1j8f38pfBs1sPsI5B6APvvss+moo47Kgs3LL788/fznP0/77rtvGjVqVBoxYkQ2K3TatGnp6aefTldddVWaN29epj1y5Mh08skn15Xfe++90xZbbFF3n40ECBAgQIAAAQIECBAgQIAAge4isGjyE2nWO18KAQLtF8g9AD3xxBPTLbfc0tyCuXPnpksvvbT5fVsvJk6c2Nau9KEPfUgA2qaOHQQIECBAgAABAgQIECBAgEDZBfr27ZuOPfbYhuvmNtts03Bt1uDyCeQegJaPSI8IECBAgAABAgQIECBAgAABAl0rsGjRovTjH//4nUYsf72Vrm1py6v3SMOHD0877bRTy41eE1jlArkHoHHLe1NTU64dGThwYK71qYwAAQIECBAgQIAAAQIECBAg0EgCsdD0a6+9ltboldKG/XJf0zp3irlLlqY/vb00zZkzJ/e6VUigowK5B6CDBg3qaBscT4AAAQIECBAgQIAAAQIECBAg0A6BfxjSO135/v7tOLJrD7l/9uK0y9Pzu7YRrk7gbwK5B6BtyUbi/9xzz6V+/fqlddZZJw0dOjT16NGjrcNtJ0CAAAECBAgQIECAAAECBAgQIECAwEoLdOqc6aeeeirttdde2fMeVl999TR27Ni02WabpWHDhqW11147HXzwwenRRx9d6U6ogAABAgQIECBAgAABAgQIECBAgAABAvUEOiUAjWeAHnfccWncuHHpxhtvTNOnT6+59syZM9Nll12WYjWwc889t2a/DQQIECBAgAABAgQIECBAgAABAgQIEFhZgU65Bf6MM85oDjXjNvdY7WvUqFFpww03TPPmzUt//vOf0xNPPJGefPLJbMGkCEvXXXfd9MUvfnFl++N8AgQIECBAgAABAgQIECBAgAABAgQINAvkHoA+/vjjKQLQKNttt12aOHFiNhO0+YotXtx0003pq1/9anrhhRfS4Ycfnvbcc8+0xhprtDjCSwIECBAgQIAAAQIECBAgQIAAAQIECKy4QO63wF900UVp0aJFaaONNkq/+tWv2gw/o8n//M//nCIEHTBgQIpFkq666qoV74kzCRAgQIAAAQIECBAgQIAAAQIECBAg0Eog9wA0bmuPctJJJ7VrNucWW2yRvvzlL2fn3H333dl3fxAgQIAAAQIECBAgQIAAAQIECBAgQCAPgVwD0MWLF6epU6dm7dpqq63a3b7KsfFsUIUAAQIECBAgQIAAAQIECBAgQIAAAQJ5CeQagPbs2TP17v3uY0Xnzp3b7jbGwkhRhgwZ0u5zHEiAAAECBAgQIECAAAECBAgQIECAAIHlCeQagMaK76NHj86u+Zvf/GZ5127ef99992WvP/jBDzZv84IAAQIECBAgQIAAAQIECBAgQIAAAQIrK5BrABqN2XrrrbM2nXzyyen5559fbvt+/etfNy9+NG7cuOUe7wACBAgQIECAAAECBAgQIECAAAECBAi0VyD3APSb3/xmGjRoUHrzzTfT+PHj0+WXX57i2aCty+zZs9Opp56aJkyYkJYuXZpi9ufee+/d+jDvCRAgQIAAAQIECBAgQIAAAQIECBAgsMIC7z6wc4VPrz1xvfXWS2eddVY64ogj0vTp07MV3o855pi08cYbpxEjRqSFCxemF198MT333HOp8uzPPn36pCuuuCL17du3tkJbCBAgQIAAAQIECBAgQIAAAQIEuqVALJj9L//yL232PdaiGTx4cNpggw3Sxz/+8bTffvuleERjo5Qf//jH6aKLLkqf+cxn0v/7f/+v3c3+yEc+kh17zz33ZBMR481OO+2U5syZk26++ea07rrrtruu7nBg7gFooP37v/97GjVqVDrwwAOzEPSNN95Ijz76aPbVGnXs2LFp4sSJye3vrWW8J0CAAAECBAgQIECAAAECBAh0b4G33367bp5UT+W///u/069+9atskl3//v3rHVK4ba+99lrWvy233LJDbYucLUpTU1PzeY8//nh66623ssmHzRu9yAQ6JQCNmnfdddc0ZcqUdMkll2Tfn3766fSHP/whxWzPkSNHZl8777xzOuCAA1KvXr0MBwECBAgQIECAAAECBAgQIECAAIE2BeKO4qFDh1btjzuN4w7k66+/Pp133nnpmmuuyWZE/td//VfVcd3hzaGHHpoiMF599dW7Q3c71MfcA9CAjlvZe/bsmYYMGZKOP/745gbFsz4baRpyc8O9IECAAAECBAgQIECAAAECBAgQ6FKBuNU9vlqXtdZaK8UdxvFYxn/7t3/LbgHvjhnUueee25rG+78J5B6AnnTSSemnP/1p+sIXvpBOOOGENGDAgGZs4WczhRcECBAgQIAAAQIECBAgQIAAAQI5CsRzNCMAnTFjRnryySfThz70oZraZ82alZ544on07LPPZuvVRHA6bNiwmuNmzpyZ4vb0CFXXXHPNbJbpAw88kM2wjMc4xmLercv//d//ZdeO+uo9g/Oll15KsSj4e9/73mzSYOvz43088/TBBx/MJhdutdVW2bH1jqu3Le68joXIN9100+wO7NbHRJ/imaFz587Njtl6663bnKgYExz/+Mc/pmeeeSZb6HyTTTZJo0ePzjxa1xt3fcdEyHgcZgTPcc7DDz+cucWt/euvv37rU1b5+9xXgb/yyiuzD8Wll16a+vXrt8o75IIECBAgQIAAAQIECBAgQIAAAQLdT+BPf/pTc6dbTsiLjREMnnbaaWnttdfOFks65JBD0j/8wz+k97znPSkm87V8lmYcH88T/cAHPpAi54q1boYPH54+//nPp/333z+NGTMm7bHHHmnBggVxaHP53ve+l51z9tlnN29r+SLqiTp/+ctfttycvV6yZEmKRcRjAfF99tknW/gprrnvvvtmgWXNCXU2RKAZ9b/66qtVe3//+99nM2Sj7xMmTMj6sO2226ZYSCn2tS7x+IBwiQD5c5/7XDr44INTPMYygttY9Hz+/PlVp0RQGwswvfzyy5nNZpttlr70pS+lPffcMzvnm9/8ZuZfddIqfpPrDND4MEXaHeVjH/uYZ3uu4sF0OQIECBAgQIAAAQIECBAgQIBAdxSIxX9OPfXUrOsR1MWMxZYlgrxf/OIXaZ111klf//rXU4R0MQs0VmCP85566ql03XXXtTwle33mmWemv/zlL+mrX/1q2muvvbJZoWeccUa22NLhhx+eLrvssppzVmTDFVdckRYtWpS+/OUvp09+8pNZiPntb387/exnP0vPP/98Nit0Re6sjhmllfo++9nPZqFkzEKN1ecfeuih9E//9E/ZIkyVGathEYFwPFYgnika+V5kfbGy/L333pstZL7hhhtmYW3Lfsbq89tvv302AzTqiLD1rrvuStdee20Kw4022igddNBBLU9Zpa9zDUBjMaMdd9wx3XnnnemRRx5JkV7HFFiFAAECBAgQIECAAAECBAgQIECAwMoIxGzMgQMHVlURt7tHQHjHHXekF154IcuhYkHulnnUrbfemoWfEfLF7e8xu7FSYkZnzIT8n//5n3Tbbbelf/zHf6zsyr5H+Pmd73wnC0ArO3bffff04Q9/OMVMyfHjx6f99tuvsmuFv0f4ecEFF6SjjjqquY699947u07cTh7hbASYHS0RcMaM0Ag1Tz755ObTYwZs3MYft6//4Ac/yPbHbe8TJ07MjonZrC2vd/TRR2czYS+++OJsVmzMVm1ZIgCNW91jJfrK7NvDDjus+Zyw6soANPd08vTTT88eSBu40bFI4BUCBAgQIECAAAECBAgQIECAAAECKyNw5JFHZjMkY5Zk5esb3/hGiscwRvgZt2zfd9992e3pLa9z1llnZW8jAGwZfsbGeF5n3GYe5fzzz8++t/wjZpLGTM+WJVZZj+tGqXc7e8tj2/s6ntsZt5e3LDEL89hjj802Rajb0RLPHJ06dWrW5+OOO67q9JjEeOGFF2aO73//+7N98WzQU045JZux2TL8rJz46U9/Onv5+uuvVzZVfY9b3SvhZ2VHzJqNEuPTlSXXGaDRkTXWWCN9//vfzwboRz/6Ubr++uuzacUjR47MnmMQK8S3VWL26A477NDWbtsJECBAgAABAgQIECBAgAABAgS6qUAEhP37989us44Jd7/+9a+zRYMi1LzpppvSRz/60boyEQJGidAvFgFqXWKhoyixiFDrEs8J7d27Nj6LW8OjxIzHPEo8YzPa17pEVhYlZmp2tMRs1yjhstpqq9Wcvuuuu6b4qpQIg2MRqZbljTfeyBZDmjx5cnYbfOxr/bzUyvGxSFLr8r73vS/btHDhwta7Vun72hFcycvHcxRuueWW5lpida1YvSq+llciiReALk/JfgIECBAgQIAAAQIECBAgQIBA9xOI2YlDhw5t7ngEcXH7+dVXX50FefGMyligqGWJXOqvf/1rtikW81lWmTZtWrawUctFveN5l/VKLFAUJW6/j9u/Bw0aVO+wdm9b3nViRfqYodn6EQDLukAlAK2EkMs6trIv1veJxwHELetPPvlk9szTyr56QXBlX3yvBMktt1UmQsbq8F1Zcg9Au7Izrk2AAAECBAgQIECAAAECBAgQINA9BCKQi+eCxmMY//d//zdb4Oe3v/1ttgBPRaBl8Bars1cCucr+Ff0+b9687NSYkVpvdmW9eluvGt/ymFhHp16pzJyMULa916nUE2FmlHi+aHtKWH3xi19MP/3pT7PDYzGpuBU+Hi0Qz0kdPHhw2m677dqsquVzV9s8qIt25B6AxgdvWQO6rH6ubFq+rLrtI0CAAAECBAgQIECAAAECBAgQKJdA3DYeK6jHzM9Y8TxWe4/VxysrpseM0XiWZswCjQWOxo0b1yGAeI5mvVLZPnbs2OZb1yu3sLcVOMZs0bZKLLZUr0SfosQq6h0NGCvP9pw+fXq9qlMsehQ5Xjzn9OMf/3j2GMsIP4cMGZI927Ry+33l5F/84hfZy7bC2spxRfye+yJI8cGKVbVW5EsAWsSPiDYRIECAAAECBAgQIECAAAECBIorELePx0rtUeIZn7E2TcvygQ98IHsbt3bXK5dddlm2fk2sCN+6xHNG6z3zMlZlj7Lllls2nxLr4kSpF2bG7esvvvhitr/eH7FSfb1yzTXXZJs7GtzGSZtvvnl27gMPPJDiUQCtSzwyIB4LUFloKWbRRtljjz1S6/Aztleed1rPI/YXueQegC6rs5G2t7VS1LLOs48AAQIECBAgQIAAAQIECBAgQIBAWwIHHHBA2mWXXbLdxx9/fHr55ZebDz3ppJOy1//5n/+ZIvRrWWKG5de+9rVsAaQRI0a03JW9jtXLzzjjjKrtjz32WPrud7+b3ZL+pS99qXnfqFGjstc33nhjqjx/MzbEreif//zn6waplZP/+Mc/phNOOKHyNvv+0EMPpVj9PW71P/HEE6v2tedNhLO77bZbevPNN1OYtJy5GYHst771rayaaFuUuMU9yn333VfT1ljv55xzzsn2x8zRRiu53wLfEuCRRx7JpszGoEdK/Morr2S743kL8eyAT3/60+nLX/5y1QNsW57vNQECBAgQIECAAAECBAgQIECAAIH2CPzgBz9IH/zgB1OsEH/44YenG264ITttp512Socddlg2MzRu9Z4wYUJ2y3wEmbfddlu2iNHWW2/dPBOy5bUGDBiQYtHu3/zmNynqiUA0bhOPEDBmZ7acARorxm+66abZqukxgzLCx9VXXz3dfvvt2QzMT37yk2nSpEktq29+HeedfvrpKWZrtrxOPGYygtF6K6w3n7yMFxdccEHafvvtU9hENhdtitA3bmePxZtiMfKjjjoqqyGC0PPOOy/F7f3hsddee6V49mjMDL3zzjtT3O4fCyNFeBqhatwq3yilUwLQ+BAceuih6Sc/+Ulq+bDZCko8wDUGNL4C9uKLL84+fJX9vhMgQIAAAQIECBAgQIAAAQIECBDoiEA8yzJme8Zsx5iFGbepR9gZJWZSRth33HHHZcFl5dbyWFjoq1/9anZevRXW99lnnzRy5MgsnLzjjjuy53Bus802We5VqbvSxnjuaISFMRs1gtXKbfJxK/r111+fbWsrAI0cLUq0P55hGmWdddZJEWDuu+++2fsV+WOzzTZLU6ZMSYccckiK2/ljsmKUmFUa/T7llFOy17EtHhUQjwk44ogjUoTD8RUl2hH5XYTK8RzVCEOjPy1nv2YHFviPHu8ElLmuQx/Tej/zmc9kMz+j3/GA1vhgxDTi4cOHZ+lyJMmBH6l55ZhInj/1qU9l77vDH2Gw6667pksvvbTucxW6g4E+EiDQcYFYaTD+pS1K/EuiZyd33NAZBAgQIFAegXjEVmWhifjlrLL4RHl6qCcECHSWQMzoiyAobuttlBKz9eJ3gAlrvrPy+fv7F77Z989enHZ5en4666yzskCySA2eOXNmeuaZZ7I7kmNxoVjJvXX59re/nY455ph00EEHZdlN5F0x+zGyrVhUaXklxuupp57KFhhqz/GV+uI6kydPzmZXRtvyLDGbdOrUqVlWF7fr1+t3XC/aELNE407uCJXXW2+9PJvRJXXlPgM0EvRf/vKXWWf23HPPdPbZZ2cPkm3du3juQCThkbzHQ2D322+/bIpt5YGxrY/3ngABAgQIECBAgAABAgQIECBAgMDKCgwbNixtt912Haom/pGtIwsRxWSVuI28oyWuE7ead0aJ29nb04dow8Ybb5x9dUY7uqLO3BdBmjhxYtaPmN0Yszpjqm29EjNDP/e5z2XPPoiHrM6ePTv98Ic/rHeobQQIECBAgAABAgQIECBAgAABAgQIEFghgdwD0Li1PUqskNWeW1DiIa6V5xzEKlMKAQIECBAgQIAAAQIECBAgQIAAAQIE8hLINQBtamrKVsGKh76OGTOm3W2MFeGjvPbaa+0+x4EECBAgQIAAAQIECBAgQIAAAQIEOkMgnpEZdy5XMqvOuIY6V51Ars8AjRWk4lkCDz/8cLbI0ZZbbtmunsQzQKN01jMOssr9QYAAAQIECBAgQIAAAQIECBAgQKAdAnvssUeKL6UcArnOAA2SHXbYIZOJW+Dbs8D8/Pnz05VXXpmdM378+Oy7PwgQIECAAAECBAgQIECAAAECBAgQIJCHQO4B6AknnJDNAr3++uvTQQcdlP7617+22c4ZM2akf/3Xf02TJ09Ou+22W9pnn33aPNYOAgQIECBAgAABAgQIECBAgAABAgQIdFQg11vg4+LPPvtsOuqoo9Jhhx2WLr/88vTzn/887bvvvimenTBixIhsVui0adPS008/na666qo0b968rM0jR45MJ598cva69R9777132mKLLVpv9p4AAQIECBAgQIAAAQIECBAgQIAAAQLLFMg9AD3xxBPTLbfc0nzRuXPnpksvvbT5fVsvJk6c2Nau9KEPfUgA2qaOHQQIECBAgAABAgQIECBAgAABAgQItCWQ+y3wbV3IdgIECBAgQIAAAQIECBAgQIAAAQIECKxqgdxngMYt701NTbn2Y+DAgbnWpzICBAgQIECAAAECBAgQIECAQCMKvL1kafrLoiWFb/obTUsL30YN7D4CuQeggwYN6j56ekqAAAECBAgQIECAAAECBAgQWIUCN89anG5+/N31VFbhZV2KQEML5B6ANrSGxhMgQIAAAQIECBAgQIAAAQIECijQp0+fdMABBxSwZctu0pgxY5Z9gL0EVoGAAHQVILsEAQIECBAgQIAAAQIECBAgQGBlBPr165cuv/zylanCuQS6rYBFkLrt0Os4AQIECBAgQIAAAQIECBAgQIAAgfILCEDLP8Z6SIAAAQIECBAgQIAAAQIECBAgQKDbCghAu+3Q6zgBAgQIECBAgAABAgQIECBAgACB8gsIQMs/xnpIgAABAgQIECBAgAABAgQIECBAoNsKCEC77dDrOAECBAgQIECAAAECBAgQIECAAIHyCwhAyz/GekiAAAECBAgQIECAAAECBAgQIECg2woIQLvt0Os4AQIECBAgQIAAAQIECBAgQIAAgfILCEDLP8Z6SIAAAQIECBAgQIAAAQIECBAgQKDbCghAu+3Q6zgBAgQIECBAgAABAgQIECBAgACB8gsIQMs/xnpIgAABAgQIECBAgAABAgQIECBAoNsKCEC77dDrOAECBAgQIECAAAECBAgQIECAAIHyC/Qufxf1kAABAgQIECBAgACB9ghMnTo1vf766+05tBDHzJo1KzU1NWVtGTp0aOrVq1ch2tWeRmy//fapR48e7TnUMQQIECBAgMBKCghAVxLQ6QQIECBAgAABAgTKInDcccelm2++uSzdKXQ/Fi5cmPr06VPoNmocAQIECBAoi4AAtCwjqR8ECBAgQIAAAQIEVlJg//33TzEzsVHKc889ly677LK03Xbbpd133z317Nk4T/hqpNmqjfJ50E4CBAgQINCWgAC0LRnbCRAgQIAAAQIECHQzgQkTJjRUj++6664sAP3oRz+aYvaqULGhhk9jCRAgQIDAKhNonH8iXWUkLkSAAAECBAgQIECAAAECBAgQIECAQFkEBKBlGUn9IECAAAECBAgQIECAAAECBAgQIECgRkAAWkNiAwECBAgQIECAAAECBAgQIECAAAECZREQgJZlJPWDAAECBAgQIECAAAECBAgQIECAAIEaAQFoDYkNBAgQIECAAAECBAgQIECAAAECBAiURUAAWpaR1A8CBAgQIECAAAECBAgQIECAAAECBGoEBKA1JDYQIECAAAECBAgQIECAAAECBAgQIFAWAQFoWUZSPwgQIECAAAECBAgQIECAAAECBAgQqBHoXbPFhlUisHTp0uw6ixYtSgsWLFgl13QRAgQaX6Cpqam5E/Ha3x/NHF4QIECAQDcXWLhwYerZ0/yObv4x0H0C7RZYvHhxqvxe3u6THEiAQMMKCEC7eOhmz56dXn/99S5uhcsTINCIAvPnz0/xpRAgQIAAge4q0PIfBmfNmtVdGfSbAIEVEIiJBEuWLFmBM51CgEAjCghAu2jUevTokV15yJAhae211+6iVrgsAQKNJhCB55w5c7JmDxw4MA0YMKDRuqC9BAgQIEAgN4Hevf/+68ywYcPMAM1NVkUEyi/Qv3//1KtXr/J3VA8JEMgE/v5/DEC6RCD+wm35P25d0ggXJUCgYQRa/k9a3Obn74+GGToNJUCAAIFOFoifkS1/Tnby5VRPgECDC3hkRoMPoOYT6KCAALSDYA4nQIAAAQIECHSmwPTp09N9993XmZdQN4HSCEyZMiXryzPPPJOuvvpqM0BLM7I60pkCI0aMSNtuu21nXkLdBAgQKJyAALRwQ6JBBAgQIECAQHcWePjhh9O+++7bnQn0nUCHBW699dYUXwoBAssXiJ8xAtDlOzmCAIFyCQhAyzWeekOAAAECBAiURODAtXunHVb3bLKSDKdudJLAnxcsSSe/vCj98xq90mfW9KtNJzGrtiQCc99Z7+ffX1xQkt7oBgECBDom4P8SOublaAIECBAgQIDAKhHYalCvtM9afVbJtVyEQKMKPDF3cRaAbjGgp/9eGnUQtXuVCbzZtFQAusq0XYgAgaIJ9Cxag7SHAAECBAgQIECAAAECBAgQIECAAAECeQkIQPOSVA8BAgQIECBAgAABAgQIECBAgAABAoUTEIAWbkg0iAABAgQIECBAgAABAgQIECBAgACBvAQEoHlJqocAAQIECBAgQIAAAQIECBAgQIAAgcIJCEALNyQaRIAAAQIECBAgQIAAAQIECBAgQIBAXgIC0Lwk1UOAAAECBAgQIECAAAECBAgQIECAQOEEBKCFGxINIkCAAAECBAgQIECAAAECBAgQIEAgLwEBaF6S6iFAgAABAgQIECBAgAABAgQIECBAoHACAtDCDYkGESBAgAABAgQIECBAgAABAgQIECCQl4AANC9J9RAgQIAAAQIECBAgQIAAAQIECBAgUDgBAWjhhkSDCBAgQIAAAQIECBAgQIAAAQIECBDIS0AAmpekeggQIECAAAECBAgQIECAAAECBAgQKJxA78K1SIMIECBAgAABAgQIEOgSgRteb0rPL1jSJddekYu+svDdtj40Z3E6/9WFK1JFl51z1Lp9Us8ePbrs+i5MgAABAgS6k4AAtDuNtr4SIECAAAECBAgQWIbAFX9dlCbNWryMI4q56563lqR73mqsAPSI90QAWkxPrSJAgAABAmUTEICWbUT1hwABAgQIECBAgMAKCpz+3r7p6HVX8GSndUigt/CzQ14OJkCAAAECKyMgAF0ZPecSIECAAAECBAgQKJHAFgN6lag3ukKAAAECBAgQeFfAIkg+CQQIECBAgAABAgQIECBAgAABAgQIlFZAAFraodUxAgQIECBAgAABAgQIECBAgAABAgQ7zDB7AAA2A0lEQVQEoD4DBAgQIECAAAECBAgQIECAAAECBAiUVkAAWtqh1TECBAgQIECAAAECBAgQIECAAAECBASgPgMECBAgQIAAAQIECBAgQIAAAQIECJRWQABa2qHVMQIECBAgQIAAAQIECBAgQIAAAQIEBKA+AwQIECBAgAABAgQIECBAgAABAgQIlFZAAFraodUxAgQIECBAgAABAgQIECBAgAABAgQEoD4DBAgQIECAAAECBAgQIECAAAECBAiUVkAAWtqh1TECBAgQIECAAAECBAgQIECAAAECBASgPgMECBAgQIAAAQIECBAgQIAAAQIECJRWQABa2qHVMQIECBAgQIAAAQIECBAgQIAAAQIEBKA+AwQIECBAgAABAgQIECBAgAABAgQIlFZAAFraodUxAgQIECBAgAABAgQIECBAgAABAgQEoD4DBAgQIECAAAECBAgQIECAAAECBAiUVkAAWtqh1TECBAgQIECAAAECBAgQIECAAAECBASgPgMECBAgQIAAAQIECBAgQIAAAQIECJRWQABa2qHVMQIECBAgQIAAAQIECBAgQIAAAQIEBKA+AwQIECBAgAABAgQIECBAgAABAgQIlFZAAFraodUxAgQIECBAgAABAgQIECBAgAABAgQEoD4DBAgQIECAAAECBAgQIECAAAECBAiUVkAAWtqh1TECBAgQIECAAAECBAgQIECAAAECBASgPgMECBAgQIAAAQIECBAgQIAAAQIECJRWQABa2qHVMQIECBAgQIAAAQIECBAgQIAAAQIEBKA+AwQIECBAgAABAgQIECBAgAABAgQIlFZAAFraodUxAgQIECBAgAABAgQIECBAgAABAgQEoD4DBAgQIECAAAECBAgQIECAAAECBAiUVkAAWtqh1TECBAgQIECAAAECBAgQIECAAAECBASgPgMECBAgQIAAAQIECBAgQIAAAQIECJRWQABa2qHVMQIECBAgQIAAAQIECBAgQIAAAQIEBKA+AwQIECBAgAABAgQIECBAgAABAgQIlFZAAFraodUxAgQIECBAgAABAgQIECBAgAABAgQEoD4DBAgQIECAAAECBAgQIECAAAECBAiUVkAAWtqh1TECBAgQIECAAAECBAgQIECAAAECBHojIECAQHcWmD9/fpo5c2bDEER733rrray9gwYNSgMHDmyYtg8dOrSh2tswsBpKgAABAgQIECBAgAABAssUEIAuk8dOAgTKLnDLLbekCRMmlL2bhejfD3/4w3TwwQcXoi0aQYAAAQIECBAgQIAAAQLdR0AA2n3GWk8JEKgjsPHGG6dDDz20zp5iblq0aFH6xS9+kRYuXJj23nvv1KdPn2I2tE6rNttsszpbbSJAgAABAgQIECBAgAABAp0rIADtXF+1EyBQcIFx48al73//+wVv5d+bN2/evPTggw+mWbNmpQsvvDDFbfAKAQIECBAgQIAAAQIECBAg0LaARZDatrGHAAECBAgQIECAAAECBAgQIECAAIEGFxCANvgAaj4BAgQIECBAgAABAgQIECBAgAABAm0LCEDbtrGHAAECBAgQIECAAAECBAgQIECAAIEGFxCANvgAaj4BAgQIECBAgAABAgQIECBAgAABAm0LCEDbtrGHAAECBAgQIECAAAECBAgQIECAAIEGFxCANvgAaj4BAgQIECBAgAABAgQIECBAgAABAm0LCEDbtrGHAAECBAgQIECAAAECBAgQIECAAIEGFxCANvgAaj4BAgQIECBAgAABAgQIECBAgAABAm0LCEDbtrGHAAECBAgQIECAAAECBAgQIECAAIEGF+jd4O3X/JIInHjiiWnBggUl6Y1uEOg8gUWLFqUZM2akt99+O51wwgmpb9++nXcxNRMokcBZZ52Vevb0774lGlJdIUCAAAECBAgQINBuAQFou6kc2JkC559/fpo7d25nXkLdBEon8J3vfKd0fdIhAp0lcPrppwtAOwtXvQQIECBAgAABAgQKLiAALfgAdZfmLX2no71HbJwGHXxEd+myfhJYYYG3Ljo3LZ0/Lw057uQVrsOJBLqLwNwr/ystenpKd+mufhIgQIAAAQIECBAgUEdAAFoHxaZVL9CjR4+U+vZLvd/7vlV/cVck0GACPeK294UL/PfSYOOmuV0j0GO1AV1zYVclQIAAAQIECBAgQKAwAh6GVZih0BACBAgQIECAAAECBAgQIECAAAECBPIWEIDmLao+AgQIECBAgAABAgQIECBAgAABAgQKIyAALcxQaAgBAgQIECBAgAABAgQIECBAgAABAnkLCEDzFlUfAQIECBAgQIAAAQIECBAgQIAAAQKFERCAFmYoNIQAAQIECBAgQIAAAQIECBAgQIAAgbwFBKB5i6qPAAECBAgQIECAAAECBAgQIECAAIHCCAhACzMUGkKAAAECBAgQIECAAAECBAgQIECAQN4CAtC8RdVHgAABAgQIECBAgAABAgQIECBAgEBhBASghRkKDSFAgAABAgQIECBAgAABAgQIECBAIG+B3nlXqD4CBAg0ksCCxx5Jsy+5oJGanNKiRVl7/3rwPg3V7kH7HZz677hzQ7VZYwkQIECAAAECBAgQIECg8QUEoI0/hnpAgMBKCPQcMDD12XjkStTg1PYK9Bw8pL2HOo4AAQIECBAgQIAAAQIECOQmULoAdMGCBem6665Lv/vd79Ibb7yRRo4cmcaOHZs+8YlPpF69enUY7u6770733Xdfmj59elqyZEl63/vel7bddtu06667drguJxAgUDyBPqO3SEO+cWrxGqZFBAgQIECAAAECBAgQIECAQC4CpQpAZ82alb7yla+kadOmZThrrrlmuvXWW7OvBx54IJ100kmpb9++7YKLIPU//uM/0uOPP54dP3jw4Oz7M888k26//fb0y1/+Mp177rlptdVWa1d9DiJAgAABAgQIECBAgAABAgQIECBAYNULlGoRpNNOOy0LP7feeut08803pxtvvDFdffXVaZNNNkn33ntvuuiii9otfPHFF2fh54gRI9Jll12WfvWrX2Vfl156aRo+fHh64okn0ne/+9121+dAAgQIECBAgAABAgQIECBAgAABAgRWvUBpAtCpU6emRx55JJuRefrpp6chQ9591twGG2yQzj///Oz290mTJqXZs2cvV3nevHnZDM+ePXumU089NY0aNar5nNGjR6czzjgjex8haxyrECBAgAABAgQIECBAgAABAgQIECBQTIHSBKD33HNPJjx+/PjUv3//Ku24FX6rrbZKCxcuTBGCLq9Mnjw5LV68OJvpudFGG9UcHtvWXnvttHTp0vT888/X7LeBAAECBAgQIECAAAECBAgQIECAAIFiCJQmAH3qqacy0bj9vV6JADTKk08+WW931bY4Np7xed5551Vtr7xpampKb775ZvZ2jTXWqGz2nQABAgQIECBAgAABAgQIECBAgACBggmUZhGkl19+OaNtK5CsbK8skLSscejRo0caOnRom4fcdttt2WzSuM0+brFvq0Qo+/DDD9fdHbNRo7z99ttpzpw5dY/pVhuXdqve6iwBAgQIrGKB+Fnbp0+fVXzVFbvc/PnzV+xEZxEgQIAAgXYILFq0yO+g7ziFQ9zVqRAg0D0EShOAzp07NxuxStDZevgqq7hXjmu9v73vX3nllXTJJZdkhx9yyCEpwtK2SjyT9Jxzzqm7e7311su2xzNE2/Nc0rqVlGjj0uQHT4mGU1cIECBQOIH4WdsoAWj846hCgAABAgQ6SyDuaPQ76LsB6JIlSzqLWb0ECBRMoBS3wMdfWpVfFlZfffW6xIMGDcq2L1iwoO7+9mycOXNm+trXvpZmzZqVPVN0zz33bM9pjiFAgAABAgQIECBAgAABAgQIECBAoIsESjEDNFZrX2211VLcMtZWwFnZ3rdv3xWifumll9IxxxyTXn311bT55ptnq8Mvr6Ltt98+nXbaaXUPi8A2VpMfOHBg84r1dQ/sJhuXNZO2mxDoJgECBAh0okA8tqZRZoAOGDCgEyVUTYAAAQLdXSB+HsbPxe5eIhuILEEhQKB7CJQiAI2hWmuttVI837OtqfyV7RE4drTEwknHH398VvdHPvKRdPrpp2fB5fLq2XTTTVN81SsRqEYA2q9fv+QXnXpCthEgQIAAgfwE4mdtowSg8f8GCgECBAgQ6CyB3r17+x30HdxwMBGnsz5l6iVQPIHS/HNHBKBRKkFna+q33nor27SsxY1anxPv77rrrnTUUUdl9e62227ZyvArEqLWq9s2AgQIECBAgAABAgQIECBAgAABAgQ6V6A0Aeg666yTST3//PN1xSrbN9tss7r762286aab0kknnZStDnfAAQekb33rW9m/EtU71jYCBAgQIECAAAECBAgQIECAAAECBIonUJoAdJdddsl077jjjhrlWCQpZnJGGTt2bM3+ehseeuihbLZnTImP298PPPDAeofZRoAAAQIECBAgQIAAAQIECBAgQIBAgQVKE4Bus802acSIEenZZ59NkyZNqiK/6qqrUqzgvuGGG6att966at/999+fbr/99vTCCy80b48Fky644IK0dOnSdNBBB6Xdd9+9eZ8XBAgQIECAAAECBAgQIECAAAECBAg0jkBpFkGKmZoHH3xwOvHEE9OZZ56ZHnzwwTRy5Mg0efLk7HUsfHDsscfWPOT4wgsvzFZ2j3M32mijbOSuu+669Morr2SvL7/88hRfbZVYEOljH/tYW7ttJ0CAAAECBAgQIECAAAECBAgQIECgCwVKE4CG4Y477pjN3IwA9O67786+YnvMDD366KPTmDFj4u1yy+9///vmYxYvXtz8ut6LuL1eIUCAAAECBAgQIECAAAECBAgQIECgmAKlCkCDeNy4cenaa6/NbnmfNm1aisWR1l133dSzZ/27/a+55pqakTn33HNrttlAgAABAgQIECBAgAABAgQIECBAgEDjCZQuAK0MwbBhw1J8KQQIECBAgAABAgQIECBAgAABAgQIdF+B+tMiu6+HnhMgQIAAAQIECBAgQIAAAQIECBAgUCIBAWiJBlNXCBAgQIAAAQIECBAgQIAAAQIECBCoFhCAVnt4R4AAAQIECBAgQIAAAQIECBAgQIBAiQQEoCUaTF0hQIAAAQIECBAgQIAAAQIECBAgQKBaQABa7eEdAQIECBAgQIAAAQIECBAgQIAAAQIlEhCAlmgwdYUAAQIECBAgQIAAAQIECBAgQIAAgWoBAWi1h3cECBAgQIAAAQIECBAgQIAAAQIECJRIQABaosHUFQIECBAgQIAAAQIECBAgQIAAAQIEqgUEoNUe3hEgQIAAAQIECBAgQIAAAQIECBAgUCIBAWiJBlNXCBAgQIAAAQIECBAgQIAAAQIECBCoFhCAVnt4R4AAAQIECBAgQIAAAQIECBAgQIBAiQQEoCUaTF0hQIAAAQIECBAgQIAAAQIECBAgQKBaQABa7eEdAQIECBAgQIAAAQIECBAgQIAAAQIlEhCAlmgwdYUAAQIECBAgQIAAAQIECBAgQIAAgWoBAWi1h3cECBAgQIAAAQIECBAgQIAAAQIECJRIQABaosHUFQIECBAgQIAAAQIECBAgQIAAAQIEqgUEoNUe3hEgQIAAAQIECBAgQIAAAQIECBAgUCIBAWiJBlNXCBAgQIAAAQIECBAgQIAAAQIECBCoFhCAVnt4R4AAAQIECBAgQIAAAQIECBAgQIBAiQQEoCUaTF0hQIAAAQIECBAgQIAAAQIECBAgQKBaQABa7eEdAQIECBAgQIAAAQIECBAgQIAAAQIlEhCAlmgwdYUAAQIECBAgQIAAAQIECBAgQIAAgWoBAWi1h3cECBAgQIAAAQIECBAgQIAAAQIECJRIQABaosHUFQIECBAgQIAAAQIECBAgQIAAAQIEqgUEoNUe3hEgQIAAAQIECBAgQIAAAQIECBAgUCIBAWiJBlNXCBAgQIAAAQIECBAgQIAAAQIECBCoFhCAVnt4R4AAAQIECBAgQIAAAQIECBAgQIBAiQQEoCUaTF0hQIAAAQIECBAgQIAAAQIECBAgQKBaQABa7eEdAQIECBAgQIAAAQIECBAgQIAAAQIlEhCAlmgwdYUAAQIECBAgQIAAAQIECBAgQIAAgWoBAWi1h3cECBAgQIAAAQIECBAgQIAAAQIECJRIQABaosHUFQIECBAgQIAAAQIECBAgQIAAAQIEqgUEoNUe3hEgQIAAAQIECBAgQIAAAQIECBAgUCIBAWiJBlNXCBAgQIAAAQIECBAgQIAAAQIECBCoFhCAVnt4R4AAAQIECBAgQIAAAQIECBAgQIBAiQQEoCUaTF0hQIAAAQIECBAgQIAAAQIECBAgQKBaQABa7eEdAQIECBAgQIAAAQIECBAgQIAAAQIlEhCAlmgwdYUAAQIECBAgQIAAAQIECBAgQIAAgWoBAWi1h3cECBAgQIAAAQIECBAgQIAAAQIECJRIQABaosHUFQIECBAgQIAAAQIECBAgQIAAAQIEqgUEoNUe3hEgQIAAAQIECBAgQIAAAQIECBAgUCIBAWiJBlNXCBAgQIAAAQIECBAgQIAAAQIECBCoFhCAVnt4R4AAAQIECBAgQIAAAQIECBAgQIBAiQQEoCUaTF0hQIAAAQIECBAgQIAAAQIECBAgQKBaQABa7eEdAQIECBAgQIAAAQIECBAgQIAAAQIlEhCAlmgwdYUAAQIECBAgQIAAAQIECBAgQIAAgWoBAWi1h3cECBAgQIAAAQIECBAgQIAAAQIECJRIQABaosHUFQIECBAgQIAAAQIECBAgQIAAAQIEqgUEoNUe3hEgQIAAAQIECBAgQIAAAQIECBAgUCIBAWiJBlNXCBAgQIAAAQIECBAgQIAAAQIECBCoFhCAVnt4R4AAAQIECBAgQIAAAQIECBAgQIBAiQQEoCUaTF0hQIAAAQIECBAgQIAAAQIECBAgQKBaQABa7eEdAQIECBAgQIAAAQIECBAgQIAAAQIlEhCAlmgwdYUAAQIECBAgQIAAAQIECBAgQIAAgWoBAWi1h3cECBAgQIAAAQIECBAgQIAAAQIECJRIQABaosHUFQIECBAgQIAAAQIECBAgQIAAAQIEqgUEoNUe3hEgQIAAAQIECBAgQIAAAQIECBAgUCIBAWiJBlNXCBAgQIAAAQIECBAgQIAAAQIECBCoFhCAVnt4R4AAAQIECBAgQIAAAQIECBAgQIBAiQQEoCUaTF0hQIAAAQIECBAgQIAAAQIECBAgQKBaQABa7eEdAQIECBAgQIAAAQIECBAgQIAAAQIlEhCAlmgwdYUAAQIECBAgQIAAAQIECBAgQIAAgWoBAWi1h3cECBAgQIAAAQIECBAgQIAAAQIECJRIQABaosHUFQIECBAgQIAAAQIECBAgQIAAAQIEqgUEoNUe3hEgQIAAAQIECBAgQIAAAQIECBAgUCIBAWiJBlNXCBAgQIAAAQIECBAgQIAAAQIECBCoFhCAVnt4R4AAAQIECBAgQIAAAQIECBAgQIBAiQQEoCUaTF0hQIAAAQIECBAgQIAAAQIECBAgQKBaQABa7eEdAQIECBAgQIAAAQIECBAgQIAAAQIlEhCAlmgwdYUAAQIECBAgQIAAAQIECBAgQIAAgWoBAWi1h3cECBAgQIAAAQIECBAgQIAAAQIECJRIQABaosHUFQIECBAgQIAAAQIECBAgQIAAAQIEqgUEoNUe3hEgQIAAAQIECBAgQIAAAQIECBAgUCIBAWiJBlNXCBAgQIAAAQIECBAgQIAAAQIECBCoFhCAVnt4R4AAAQIECBAgQIAAAQIECBAgQIBAiQQEoCUaTF0hQIAAAQIECBAgQIAAAQIECBAgQKBaQABa7eEdAQIECBAgQIAAAQIECBAgQIAAAQIlEhCAlmgwdYUAAQIECBAgQIAAAQIECBAgQIAAgWoBAWi1h3cECBAgQIAAAQIECBAgQIAAAQIECJRIQABaosHUFQIECBAgQIAAAQIECBAgQIAAAQIEqgV6V7/1blULNDU1pYULF67qy7oeAQIECBDoVgLxs3bp0qUN0edFixY1RDs1kgABAgQaU2Dx4sV+B31n6MJBIUCg+wgIQLtorCu/hL311ltp5syZXdSK4ly24lGcFmkJAQIECJRJIH7W9unTpyG6NGfOnIZop0YSIECAQGMKxD8K+h00pQULFghBG/MjrNUEVkhAALpCbCt/Uo8ePbJKBg8enIYNG7byFTZ4DRWPBu+G5hMgQIBAQQXiZ22jBKCDBg0qqKJmESBAgEAZBPr27et30HcGsl+/fqlXr15lGFJ9IECgHQIC0HYgdeYhvXv3TvEDSCFAgAABAgQ6TyB+1jZKANoo7ey80VIzAQIECHSmQIR+fgdNws/O/JCpm0ABBSyCVMBB0SQCBAgQIECAAAECBAgQIECAAAECBPIREIDm46gWAgQIECBAgAABAgQIECBAgAABAgQKKCAALeCgaBIBAgQIECBAgAABAgQIECBAgAABAvkICEDzcVQLAQIECBAgQIAAAQIECBAgQIAAAQIFFBCAFnBQNIkAAQIECBAgQIAAAQIECBAgQIAAgXwEBKD5OKqFAAECBAgQIECAAAECBAgQIECAAIECCghACzgomkSAAAECBAgQIECAAAECBAgQIECAQD4CAtB8HNVCgAABAgQIECBAgAABAgQIECBAgEABBQSgBRwUTSJAgAABAgQIECBAgAABAgQIECBAIB8BAWg+jmohQIAAAQIECBAgQIAAAQIECBAgQKCAAgLQAg6KJhEgQIAAAQIECBAgQIAAAQIECBAgkI+AADQfR7UQIECAAAECBAgQIECAAAECBAgQIFBAAQFoAQdFkwgQIECAAAECBAgQIECAAAECBAgQyEdAAJqPo1oIECBAgAABAgQIECBAgAABAgQIECiggAC0gIOiSQQIECBAgAABAgQIECBAgAABAgQI5CMgAM3HUS0ECBAgQIAAAQIECBAgQIAAAQIECBRQQABawEHRJAIECBAgQIAAAQIECBAgQIAAAQIE8hEQgObjqBYCBAgQIECAAAECBAgQIECAAAECBAooIAAt4KBoEgECBAgQIECAAAECBAgQIECAAAEC+QgIQPNxVAsBAgQIECBAgAABAgQIECBAgAABAgUUEIAWcFA0iQABAgQIECBAgAABAgQIECBAgACBfAQEoPk4qoUAAQIECBAgQIAAAQIECBAgQIAAgQIKCEALOCiaRIAAAQIECBAgQIAAAQIECBAgQIBAPgIC0Hwc1UKAAAECBAgQIECAAAECBAgQIECAQAEFBKAFHBRNIkCAAAECBAgQIECAAAECBAgQIEAgHwEBaD6OaiFAgAABAgQIECBAgAABAgQIECBAoIACAtACDoomESBAgAABAgQIECBAgAABAgQIECCQj4AANB9HtRAgQIAAAQIECBAgQIAAAQIECBAgUEABAWgBB0WTCBAgQIAAAQIECBAgQIAAAQIECBDIR0AAmo+jWggQIECAAAECBAgQIECAAAECBAgQKKCAALSAg6JJBAgQIECAAAECBAgQIECAAAECBAjkIyAAzcdRLQQIECBAgAABAgQIECBAgAABAgQIFFBAAFrAQdEkAgQIECBAgAABAgQIECBAgAABAgTyERCA5uOoFgIECBAgQIAAAQIECBAgQIAAAQIECiggAC3goGgSAQIECBAgQIAAAQIECBAgQIAAAQL5CAhA83FUCwECBAgQIECAAAECBAgQIECAAAECBRQQgBZwUDSJAAECBAgQIECAAAECBAgQIECAAIF8BASg+TiqhQABAgQIECBAgAABAgQIECBAgACBAgoIQAs4KJpEgAABAgQIECBAgAABAgQIECBAgEA+AgLQfBzVQoAAAQIECBAgQIAAAQIECBAgQIBAAQUEoAUcFE0iQIAAAQIECBAgQIAAAQIECBAgQCAfAQFoPo5qIUCAAAECBAgQIECAAAECBAgQIECggAIC0AIOiiYRIECAAAECBAgQIECAAAECBAgQIJCPgAA0H0e1ECBAgAABAgQIECBAgAABAgQIECBQQAEBaAEHRZMIECBAgAABAgQIECBAgAABAgQIEMhHQACaj6NaCBAgQIAAAQIECBAgQIAAAQIECBAooIAAtICDokkECBAgQIAAAQIECBAgQIAAAQIECOQjIADNx1EtBAgQIECAAAECBAgQIECAAAECBAgUUEAAWsBB0SQCBAgQIECAAAECBAgQIECAAAECBPIREIDm46gWAgQIECBAgAABAgQIECBAgAABAgQKKCAALeCgaBIBAgQIECBAgAABAgQIECBAgAABAvkICEDzcVQLAQIECBAgQIAAAQIECBAgQIAAAQIFFBCAFnBQNIkAAQIECBAgQIAAAQIECBAgQIAAgXwEBKD5OKqFAAECBAgQIECAAAECBAgQIECAAIECCghACzgomkSAAAECBAgQIECAAAECBAgQIECAQD4CAtB8HNVCgAABAgQIECBAgAABAgQIECBAgEABBQSgBRwUTSJAgAABAgQIECBAgAABAgQIECBAIB8BAWg+jmohQIAAAQIECBAgQIAAAQIECBAgQKCAAgLQAg6KJhEgQIAAAQIECBAgQIAAAQIECBAgkI+AADQfR7UQIECAAAECBAgQIECAAAECBAgQIFBAAQFoAQdFkwgQIECAAAECBAgQIECAAAECBAgQyEdAAJqPo1oIECBAgAABAgQIECBAgAABAgQIECiggAC0gIOiSQQIECBAgAABAgQIECBAgAABAgQI5CMgAM3HUS0ECBAgQIAAAQIECBAgQIAAAQIECBRQQABawEHRJAIECBAgQIAAAQIECBAgQIAAAQIE8hEQgObjqBYCBAgQIECAAAECBAgQIECAAAECBAooIAAt4KBoEgECBAgQIECAAAECBAgQIECAAAEC+QgIQPNxVAsBAgQIECBAgAABAgQIECBAgAABAgUUEIAWcFA0iQABAgQIECBAgAABAgQIECBAgACBfAQEoPk4qoUAAQIECBAgQIAAAQIECBAgQIAAgQIKCEALOCiaRIAAAQIECBAgQIAAAQIECBAgQIBAPgIC0Hwc1UKAAAECBAgQIECAAAECBAgQIECAQAEFBKAFHBRNIkCAAAECBAgQIECAAAECBAgQIEAgHwEBaD6OaiFAgAABAgQIECBAgAABAgQIECBAoIACAtACDoomESBAgAABAgQIECBAgAABAgQIECCQj4AANB9HtRAgQIAAAQIECBAgQIAAAQIECBAgUEABAWgBB0WTCBAgQIAAAQIECBAgQIAAAQIECBDIR0AAmo+jWggQIECAAAECBAgQIECAAAECBAgQKKCAALSAg6JJBAgQIECAAAECBAgQIECAAAECBAjkIyAAzcdRLQQIECBAgAABAgQIECBAgAABAgQIFFBAAFrAQdEkAgQIECBAgAABAgQIECBAgAABAgTyERCA5uOoFgIECBAgQIAAAQIECBAgQIAAAQIECijQu4BtWqkmLViwIF133XXpd7/7XXrjjTfSyJEj09ixY9MnPvGJ1KtXrw7X/Yc//CFde+216c9//nMaOHBg+uAHP5h23nnntPHGG3e4LicQIECAAAECBAgQIECAAAECBAgQILBqBUoVgM6aNSt95StfSdOmTcsU11xzzXTrrbdmXw888EA66aSTUt++fdstHEHqhRdemB0/aNCgtHDhwvTYY4+la665Jp199tnpwx/+cLvrciABAgQIECBAgAABAgQIECBAgAABAqteoFS3wJ922mlZ+Ln11lunm2++Od14443p6quvTptsskm6995700UXXdRu4cmTJ2fHR2B6xhlnpFtuuSULUo888sg0f/78dMwxx6TXXnut3fU5kAABAgQIECBAgAABAgQIECBAgACBVS9QmgB06tSp6ZFHHkmrrbZaOv3009OQIUMyzQ022CCdf/752e3vkyZNSrNnz26X8hVXXJGWLl2avvCFL6Qdd9wx9ejRI/Xp0yd99rOfTRMmTEiLFi1KN9xwQ7vqchABAgQIECBAgAABAgQIECBAgAABAl0jUJoA9J577skEx48fn/r371+lGbfCb7XVVtkt7BGCLq/MmzcvC1PjuN12263m8Mq2mGXa1NRUs98GAgQIECBAgAABAgQIECBAgAABAgSKIVCaAPSpp57KROP293olAtAoTz75ZL3dVduefvrpbPbn8OHD0/rrr1+1L96MHj06rb766unNN99ML730Us1+GwgQIECAAAECBAgQIECAAAECBAgQKIZAaRZBevnllzPRNdZYo65sZXtlgaS6B/1t4/LqisOivridPupra0X4WEH+8ccfr3upWK0+yttvv53mzp1b9xgbCRAgQIAAgXwE4mdtPMqmEUr8v4FCgAABAgQ6SyDuYvQ7aMoeaxePvVMIEOgeAqUJQCt/gVeCztbDN3jw4GxT5bjW+1u+rxzTVl1xbHvqe/DBB7PV4lvWXXm93nrrZS/jdvu33nqrsrnbfl+yZElqevG59MbxR3ZbAx0nQIAAgfwFFr8+M6s0ftY2SgAaiy1GOXn6wnTBqwuz1/4gQIAAAQIrK7D4bxXEehZ+B303AI3fQxUCBLqHQCkC0PhLqzJbIm5Nr1cGDRqUba7MvKx3TGVbhJJR2qor9lXqq1w3tikrLvC+dx43UHFf8VqcSYAAAQIEWgmss3a2IRYzbJQyYMCAFI/hifLu/5E0Ssu1kwABAgSKLhA/XdZaa62iN1P7CBAgkLtAKQLQnj17Zqu/x4yJtgLOyva+ffsuF3HgwIHZMQsXtj3rolJfv3792qwvnkf6jW98o+7+qPvb3/52il9yKrNJ6x7YTTY++uij3aSnuklg5QTi747KP7zE3z/L+jto5a7kbAIEukrgU5/6VIovhQCB5QvEnVuLF787ry0mKMTvBQoBAgTaIxB3hvg7oz1SjiFQDoFSBKAxFPGvWPE8znguZ71S2V4JN+sdU9lW+RexZd0W0J76Nt988xRf9UosnhQBaKxY35421avDNgIEup9AzGKrBKDxDzr+/uh+nwE9JkCAAIG/C8QEiEoAGhMLevXq9fedXhEgQGAZAhGANtIdIsvoil0ECLRDoDT/RFoJLSvBZOu+V8LMoUOHtt5V8355dcUJHamv5gI2ECBAgAABAgQIECBAgAABAgQIECCwSgRKE4Cus846Gdjzzz9fF66yfbPNNqu7v+XGSl0xozQeEN26vPnmm+n111/PpsuPHDmy9W7vCRAgQIAAAQIECBAgQIAAAQIECBAoiEBpAtBddtklI73jjjtqaGORpLvuuivbPnbs2Jr9rTesv/76afTo0WnOnDnp4Ycfbr073X333dmtNnFM3GqjECBAgAABAgQIECBAgAABAgQIECBQTIHSBKDbbLNNGjFiRHr22WfTpEmTqrSvuuqqNHPmzLThhhumWJioZbn//vvT7bffnl544YWWm9M+++yTvf/Rj35U9VzRGTNmpJ/97GfZvs9+9rNV53hDgAABAgQIECBAgAABAgQIECBAgECxBEqzCFI8vPjggw9OJ554YjrzzDPTgw8+mOL29MmTJ2ev4wHHxx57bM1Dji+88ML06quvZudutNFGzaMzfvz4FLfLP/300+mggw5KH//4x1NTU1OKGaYRpm6//fZp5513bj7eCwIECBAgQIAAAQIECBAgQIAAAQIEiidQmgA0aHfcccd0wQUXZAFo3KYeX1FiZujRRx+dxowZk71vzx+xguR3v/vdrL7bbrstxSzSKLF9woQJ6dBDD82eAdqeuhxDgAABAgQIECBAgAABAgQIECBAgEDXCJQqAA3CcePGpWuvvTabpRmLGMWCRuuuu26bYeU111zTpny/fv3S8ccfn4455pj03HPPpaVLl6bhw4engQMHtnmOHQQIECBAgAABAgQIECBAgAABAgQIFEegdAFohXbYsGEpvvIovXv3TqNGjcqjKnUQIECAAAECBAgQIECAAAECBAgQILAKBUqzCNIqNHMpAgQIECBAgAABAgQIECBAgAABAgQaREAA2iADpZkECBAgQIAAAQIECBAgQIAAAQIECHRcQADacTNnECBAgAABAgQIECBAgAABAgQIECDQIAIC0AYZKM0kQIAAAQIECBAgQIAAAQIECBAgQKDjAgLQjps5gwABAgQIECBAgAABAgQIECBAgACBBhEQgDbIQGkmAQIECBAgQIAAAQIECBAgQIAAAQIdFxCAdtzMGQQIECBAgAABAgQIECBAgAABAgQINIiAALRBBkozCRAgQIAAAQIECBAgQIAAAQIECBDouIAAtONmziBAgAABAgQIECBAgAABAgQIECBAoEEEBKANMlCaSYAAAQIECBAgQIAAAQIECBAgQIBAxwUEoB03cwYBAgQIECBAgAABAgQIECBAgAABAg0iIABtkIHSTAIECBAgQIAAAQIECBAgQIAAAQIEOi4gAO24mTMIECBAgAABAgQIECBAgAABAgQIEGgQAQFogwyUZhIgQIAAAQIECBAgQIAAAQIECBAg0HEBAWjHzZxBgAABAgQIECBAgAABAgQIECBAgECDCAhAG2SgNJMAAQIECBAgQIAAAQIECBAgQIAAgY4LCEA7buYMAgQIECBAgAABAgQIECBAgAABAgQaREAA2iADpZkECBAgQIAAAQIECBAgQIAAAQIECHRcQADacTNnECBAgAABAgQIECBAgAABAgQIECDQIAIC0AYZKM0kQIAAAQIECBAgQIAAAQIECBAgQKDjAgLQjps5gwABAgQIECBAgAABAgQIECBAgACBBhEQgDbIQGkmAQIECBAgQIAAAQIECBAgQIAAAQIdFxCAdtzMGQQIECBAgAABAgQIECBAgAABAgQINIiAALRBBkozCRAgQIAAAQIECBAgQIAAAQIECBDouEDvjp/ijDwF7r333vTaa6/lWaW6CBAoscDChQvTvHnzsh72798/xZdCgAABAgS6q8Ds2bPT4sWLs+4PHjw49expfkd3/SzoN4GOCvzpT3/q6CmOJ0CggQUEoF08eD/5yU+6uAUuT4AAAQIECBAgQIAAAQIEup/AJpts0v06rccEuqlAj6XvlG7a9y7v9qJFi7q8DRpAgEBjCdxwww3p5JNPzhp99NFHp/3337+xOqC1BAgQIEAgR4EDDzwwPfbYY1mNd999dxo6dGiOtauKAIGyC/To0SP17m1eWNnHWf8IhID/0rvwc9CnT58uvLpLEyDQqAJNTU3NTff3SDOFFwQIECDQDQXi9vfKz8UIMfxc7IYfAl0mQIAAAQLtEPCQnHYgOYQAAQIECBAgQIAAAQIECBAgQIAAgcYUEIA25rhpNQECBAgQIECAAAECBAgQIECAAAEC7RAQgLYDySEECBAgQIAAAQIECBAgQIAAAQIECDSmgAC0McdNqwkQIECAAAECBAgQIECAAAECBAgQaIeAALQdSA4hQIAAAQIECBAgQIAAAQIECBAgQKAxBXosfac0ZtO1mgABAt1PYPr06WnKlClZx0eNGpU22mij7oegxwQIECBA4G8CDz/8cHrjjTeydzvvvHPq27cvGwIECBAgQIBAjYAAtIbEBgIECBAgQIAAAQIECBAgQIAAAQIEyiLgFviyjKR+ECBAgAABAgQIECBAgAABAgQIECBQIyAArSGxgQABAgQIECBAgAABAgQIECBAgACBsggIQMsykvpBgAABAgQIECBAgAABAgQIECBAgECNgAC0hsQGAgQIECBAgAABAgQIECBAgAABAgTKIiAALctI6gcBAgQIECBAgAABAgQIECBAgAABAjUCAtAaEhsIECBAgAABAgQIECBAgAABAgQIECiLgAC0LCOpHwQIECBAgAABAgQIECBAgAABAgQI1AgIQGtIbCBAgAABAgQIECBAgAABAgQIECBAoCwCAtCyjKR+ECBAgAABAgQIECBAgAABAgQIECBQIyAArSGxgQABAgQIECBAgAABAgQIECBAgACBsggIQMsykvpBgAABAgQIECBAgAABAgQIECBAgECNgAC0hsQGAgQIECBAgAABAgQIECBAgAABAgTKIiAALctI6gcBAgQIECBAgAABAgQIECBAgAABAjUCAtAaEhsIECBAgAABAgQIECBAgAABAgQIECiLgAC0LCOpHwQIECBAgAABAgQIECBAgAABAgQI1AgIQGtIbCBAgAABAgQIECBAgAABAgQIECBAoCwCAtCyjKR+ECBAgAABAgQIECBAgAABAgQIECBQIyAArSGxgQABAgQIECBAgAABAgQIECBAgACBsggIQMsykvpBgAABAgQIECBAgAABAgQIECBAgECNgAC0hsQGAgQIECBAgAABAgQIECBAgAABAgTKIiAALctI6gcBAgQIECBAgAABAgQIECBAgAABAjUCAtAaEhsIECBAgAABAgQIECBAgAABAgQIECiLgAC0LCOpHwQIECBAgAABAgQIECBAgAABAgQI1AgIQGtIbCBAgAABAgQIECBAgAABAgQIECBAoCwCAtCyjKR+ECBAgAABAgQIECBAgAABAgQIECBQIyAArSGxgQABAgQIECBAgAABAgQIECBAgACBsggIQMsykvpBgAABAgQIECBAgAABAgQIECBAgECNgAC0hsQGAgQIECBAgAABAgQIECBAgAABAgTKIiAALctI6gcBAgQIECBAgAABAgQIECBAgAABAjUCAtAaEhsIECBAgAABAgQIECBAgAABAgQIECiLgAC0LCOpHwQIECBAgAABAgQIECBAgAABAgQI1AgIQGtIbCBAgAABAgQIECBAgAABAgQIECBAoCwCAtCyjKR+ECBAgAABAgQIECBAgAABAgQIECBQIyAArSGxgQABAgQIECBAgAABAgQIECBAgACBsggIQMsykvpBgAABAgQIECBAgAABAgQIECBAgECNgAC0hsQGAgQIECBAgAABAgQIECBAgAABAgTKIiAALctI6gcBAgQIECBAgAABAgQIECBAgAABAjUCAtAaEhsIECBAgAABAgQIECBAgAABAgQIECiLgAC0LCOpHwQIECBAgAABAgQIECBAgAABAgQI1AgIQGtIbCBAgAABAgQIECBAgAABAgQIECBAoCwCAtCyjKR+ECBAgAABAgQIECBAgAABAgQIECBQIyAArSGxgQABAgQIECBAgAABAgQIECBAgACBsggIQMsykvpBgAABAgQIECBAgAABAgQIECBAgECNgAC0hsQGAgQIECBAgAABAgQIECBAgAABAgTKIiAALctI6gcBAgQIECBAgAABAgQIECBAgAABAjUCAtAaEhsIECBAgAABAgQIECBAgAABAgQIECiLgAC0LCOpHwQIECBAgAABAgQIECBAgAABAgQI1AgIQGtIbCBAgAABAgQIECBAgAABAgQIECBAoCwCAtCyjKR+ECBAgAABAgQIECBAgAABAgQIECBQIyAArSGxgQABAgQIECBAgAABAgQIECBAgACBsggIQMsykvpBgAABAgQIECBAgAABAgQIECBAgECNgAC0hsQGAgQIECBAgAABAgQIECBAgAABAgTKIiAALctI6gcBAgQIECBAgAABAgQIECBAgAABAjUCAtAaEhsIECBAgAABAgQIECBAgAABAgQIECiLgAC0LCOpHwQIECBAgAABAgQIECBAgAABAgQI1AgIQGtIbCBAgAABAgQIECBAgAABAgQIECBAoCwCAtCyjKR+ECBAgAABAgQIECBAgAABAgQIECBQIyAArSGxgQABAgQIECBAgAABAgQIECBAgACBsggIQMsykvpBgAABAgQIECBAgAABAgQIEPj/7dghEQAAAAKx/q0J8A24WRzDQYAAgQg4QEMiIECAAAECBAgQIECAAAECBAgQIEDgRcAB+rKkHgQIECBAgAABAgQIECBAgAABAgQIRMABGhIBAQIECBAgQIAAAQIECBAgQIAAAQIvAg7QlyX1IECAAAECBAgQIECAAAECBAgQIEAgAg7QkAgIECBAgAABAgQIECBAgAABAgQIEHgRcIC+LKkHAQIECBAgQIAAAQIECBAgQIAAAQIRcICGRECAAAECBAgQIECAAAECBAgQIECAwIuAA/RlST0IECBAgAABAgQIECBAgAABAgQIEIiAAzQkAgIECBAgQIAAAQIECBAgQIAAAQIEXgQcoC9L6kGAAAECBAgQIECAAAECBAgQIECAQAQcoCERECBAgAABAgQIECBAgAABAgQIECDwIuAAfVlSDwIECBAgQIAAAQIECBAgQIAAAQIEIuAADYmAAAECBAgQIECAAAECBAgQIECAAIEXAQfoy5J6ECBAgAABAgQIECBAgAABAgQIECAQAQdoSAQECBAgQIAAAQIECBAgQIAAAQIECLwIOEBfltSDAAECBAgQIECAAAECBAgQIECAAIEIDD1yFUEGkYuGAAAAAElFTkSuQmCC" width="672" /></p>
<pre class="r"><code>#house

housePlotP &lt;- cces2.2
housePlotP$pid &lt;- factor(housePlotP$pid)
housePlotP &lt;- summarySE(cces2.2, measurevar=&quot;RCO327_p&quot;, groupvars=c(&quot;pid&quot;), na.rm=TRUE)
housePlotP$office &lt;- &quot;u.s. house&quot;
housePlotP$proportion &lt;- housePlotP$RCO327_p
housePlotP$RCO327_p &lt;- NULL

house_party_PlotP &lt;- ggplot(housePlotP, aes(x=pid, y=proportion, fill=pid)) 
house_party_PlotP + 
  scale_fill_manual(values=c(&quot;blue&quot;, &quot;red&quot;),
                    name=&quot;Party&quot;, # Legend label, use darker colors
                    breaks=c(&quot;Democrat&quot;, &quot;Republican&quot;),
                    labels=c(&quot;Democrat&quot;, &quot;Republican&quot;)) +
  geom_bar(position=position_dodge(), stat=&quot;identity&quot;,
           colour=&quot;black&quot;, # Use black outlines,
           size=.3) +      # Thinner lines
  geom_errorbar(aes(ymin=proportion-se, ymax=proportion+se),
                size=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9))+
  xlab(&quot;&quot;) +
  scale_x_discrete(breaks=c(&quot;Democrat&quot;,&quot;Republican&quot;),
                   labels=c(&quot;&quot;, &quot;&quot;)) +
  ylab(&quot;proportion&quot;) +
  ylim(0,0.8) +
  ggtitle(&quot;&quot;) +
  theme_bw()</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>### Combining dataframes ##

localofficePlotP &lt;- rbind(sbPlotP, leoPlotP, sherPlotP, daPlotP, housePlotP)
localofficePlotP$office &lt;- ordered(localofficePlotP$office, 
                                   levels = c(&quot;school board&quot;, &quot;sheriff&quot;,
                                              &quot;district attorney&quot;, 
                                              &quot;local election official&quot;,
                                              &quot;u.s. house&quot;))

local_p_partyplot &lt;- ggplot(localofficePlotP, aes(x=office, y=proportion, fill=pid)) + 
  xlab(&quot;Office&quot;) +
  ylab(&quot;Proportion&quot;) +
  scale_fill_manual(values=c(&quot;blue&quot;, &quot;red&quot;),
                    name=&quot;Party&quot;, # Legend label, use darker colors
                    breaks=c(&quot;Democrat&quot;, &quot;Republican&quot;),
                    labels=c(&quot;Democrat&quot;, &quot;Republican&quot;)) +
  geom_bar(position=position_dodge(), stat=&quot;identity&quot;,
           colour=&quot;black&quot;, # Use black outlines,
           size=.3) +      # Thinner lines
  geom_errorbar(aes(ymin=proportion-se, ymax=proportion+se),
                size=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9))+
  ggtitle(&quot;Preference for Partisan elections&quot;) +
  ylim(0,1)+
  theme_bw()

local_p_partyplot</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>##################################################################

# Figure 3. Inter-party differences #

##################################################################

sbContrast &lt;- cces2.2
sbContrast &lt;- subset(sbContrast, c(Pres_Vote == &quot;Solid Democrat&quot; | Pres_Vote == &quot;Solid Republican&quot;))

sbContrast$pid &lt;- factor(sbContrast$pid)
sbContrastD &lt;- subset(sbContrast, pid==&quot;Democrat&quot;)
sbContrastR &lt;- subset(sbContrast, pid==&quot;Republican&quot;)

sbContrastD &lt;- summarySE(sbContrastD, measurevar=&quot;RCO322_np&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
sbContrastD$office &lt;- &quot;school board&quot;
sbContrastD$party &lt;- &quot;Democrat&quot;
sbContrastD$proportion &lt;- sbContrastD$RCO322_np
sbContrastD$RCO322_np &lt;- NULL


sbContrastR &lt;- summarySE(sbContrastR, measurevar=&quot;RCO322_np&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
sbContrastR$office &lt;- &quot;school board&quot;
sbContrastR$party &lt;- &quot;Republican&quot;
sbContrastR$proportion &lt;- sbContrastR$RCO322_np
sbContrastR$RCO322_np &lt;- NULL

sbContrastDR &lt;- rbind(sbContrastD, sbContrastR)
sbContrastDR$Pres_Vote &lt;- ordered(sbContrastDR$Pres_Vote, 
                                  levels = c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;))

pd &lt;- position_dodge(0.1)
sbCD &lt;- ggplot(sbContrastDR, aes(x=Pres_Vote, y=proportion, colour=party, group=party, legend=&quot;right&quot;)) + 
  geom_errorbar(aes(ymin=proportion-ci, ymax=proportion+ci), colour=&quot;black&quot;, width=.1, position=pd) +
  geom_line(position=pd) +
  geom_point(position=pd, size=3, shape=21, fill=&quot;white&quot;) + 
  xlab(&quot;&quot;) +
  ylab(&quot;&quot;) +
  scale_color_manual(values=c(&quot;Democrat&quot; = &quot;blue&quot;, 
                              &quot;Republican&quot; = &quot;red&quot;)) +
  scale_x_discrete(breaks=c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;),
                   labels=c(&quot;Solid\nDemocrat&quot;, &quot;Solid\nRepublican&quot;)) +
  ggtitle(&quot;School Board&quot;) +
  ylim(0,1) +                        
  theme_bw() 

# leo #
leoContrast &lt;- cces2.2
leoContrast &lt;- subset(leoContrast, c(Pres_Vote == &quot;Solid Democrat&quot; | Pres_Vote == &quot;Solid Republican&quot;))

leoContrast$pid &lt;- factor(leoContrast$pid)
leoContrastD &lt;- subset(leoContrast, pid==&quot;Democrat&quot;)
leoContrastR &lt;- subset(leoContrast, pid==&quot;Republican&quot;)

leoContrastD &lt;- summarySE(leoContrastD, measurevar=&quot;RCO321_np&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
leoContrastD$office &lt;- &quot;local election official&quot;
leoContrastD$party &lt;- &quot;Democrat&quot;
leoContrastD$proportion &lt;- leoContrastD$RCO321_np
leoContrastD$RCO321_np &lt;- NULL


leoContrastR &lt;- summarySE(leoContrastR, measurevar=&quot;RCO321_np&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
leoContrastR$office &lt;- &quot;local election official&quot;
leoContrastR$party &lt;- &quot;Republican&quot;
leoContrastR$proportion &lt;- leoContrastR$RCO321_np
leoContrastR$RCO321_np &lt;- NULL

leoContrastDR &lt;- rbind(leoContrastD, leoContrastR)
leoContrastDR$Pres_Vote &lt;- ordered(leoContrastDR$Pres_Vote, 
                                   levels = c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;))

pd &lt;- position_dodge(0.1)
leoCD &lt;- ggplot(leoContrastDR, aes(x=Pres_Vote, y=proportion, colour=party, group=party, legend=&quot;right&quot;)) + 
  geom_errorbar(aes(ymin=proportion-ci, ymax=proportion+ci), colour=&quot;black&quot;, width=.1, position=pd) +
  geom_line(position=pd) +
  geom_point(position=pd, size=3, shape=21, fill=&quot;white&quot;) + # 21 is filled circle
  xlab(&quot;&quot;) +
  ylab(&quot;&quot;) +
  scale_color_manual(values=c(&quot;Democrat&quot; = &quot;blue&quot;, 
                              &quot;Republican&quot; = &quot;red&quot;)) +
  scale_x_discrete(breaks=c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;),
                   labels=c(&quot;Solid\nDemocrat&quot;, &quot;Solid\nRepublican&quot;)) + # Use darker colors, lightness=40
  ggtitle(&quot;Local Election Official&quot;) +
  ylim(0,1) +                        # Expand y range        # Set tick every 4
  theme_bw() 


# sheriff #
sherContrast &lt;- cces2.2
sherContrast &lt;- subset(leoContrast, c(Pres_Vote == &quot;Solid Democrat&quot; | Pres_Vote == &quot;Solid Republican&quot;))

sherContrast$pid &lt;- factor(sherContrast$pid)
sherContrastD &lt;- subset(sherContrast, pid==&quot;Democrat&quot;)
sherContrastR &lt;- subset(sherContrast, pid==&quot;Republican&quot;)

sherContrastD &lt;- summarySE(sherContrastD, measurevar=&quot;RCO323_np&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
sherContrastD$office &lt;- &quot;sheriff&quot;
sherContrastD$party &lt;- &quot;Democrat&quot;
sherContrastD$proportion &lt;- sherContrastD$RCO323_np
sherContrastD$RCO323_np &lt;- NULL


sherContrastR &lt;- summarySE(sherContrastR, measurevar=&quot;RCO323_np&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
sherContrastR$office &lt;- &quot;sheriff&quot;
sherContrastR$party &lt;- &quot;Republican&quot;
sherContrastR$proportion &lt;- sherContrastR$RCO323_np
sherContrastR$RCO323_np &lt;- NULL

sherContrastDR &lt;- rbind(sherContrastD, sherContrastR)
sherContrastDR$Pres_Vote &lt;- ordered(sherContrastDR$Pres_Vote, 
                                    levels = c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;))

pd &lt;- position_dodge(0.1)
sherCD &lt;- ggplot(sherContrastDR, aes(x=Pres_Vote, y=proportion, colour=party, group=party, legend=&quot;right&quot;)) + 
  geom_errorbar(aes(ymin=proportion-ci, ymax=proportion+ci), colour=&quot;black&quot;, width=.1, position=pd) +
  geom_line(position=pd) +
  geom_point(position=pd, size=3, shape=21, fill=&quot;white&quot;) + 
  xlab(&quot;&quot;) +
  ylab(&quot;&quot;) +
  scale_color_manual(values=c(&quot;Democrat&quot; = &quot;blue&quot;, 
                              &quot;Republican&quot; = &quot;red&quot;)) +  
  scale_x_discrete(breaks=c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;),
                   labels=c(&quot;Solid\nDemocrat&quot;, &quot;Solid\nRepublican&quot;)) + 
  ggtitle(&quot;Sheriff&quot;) +
  ylim(0,1) +                       
  theme_bw() 


# D.A. #
daContrast &lt;- cces2.2
daContrast &lt;- subset(daContrast, c(Pres_Vote == &quot;Solid Democrat&quot; | Pres_Vote == &quot;Solid Republican&quot;))

daContrast$pid &lt;- factor(daContrast$pid)
daContrastD &lt;- subset(daContrast, pid==&quot;Democrat&quot;)
daContrastR &lt;- subset(daContrast, pid==&quot;Republican&quot;)

daContrastD &lt;- summarySE(daContrastD, measurevar=&quot;RCO324_np&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
daContrastD$office &lt;- &quot;d.a.&quot;
daContrastD$party &lt;- &quot;Democrat&quot;
daContrastD$proportion &lt;- daContrastD$RCO324_np
daContrastD$RCO324_np &lt;- NULL


daContrastR &lt;- summarySE(daContrastR, measurevar=&quot;RCO324_np&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
daContrastR$office &lt;- &quot;d.a.&quot;
daContrastR$party &lt;- &quot;Republican&quot;
daContrastR$proportion &lt;- daContrastR$RCO324_np
daContrastR$RCO324_np &lt;- NULL

daContrastDR &lt;- rbind(daContrastD, daContrastR)
daContrastDR$Pres_Vote &lt;- ordered(daContrastDR$Pres_Vote, 
                                  levels = c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;))

pd &lt;- position_dodge(0.1)
daCD &lt;- ggplot(daContrastDR, aes(x=Pres_Vote, y=proportion, colour=party, group=party, legend=&quot;right&quot;)) + 
  geom_errorbar(aes(ymin=proportion-ci, ymax=proportion+ci), colour=&quot;black&quot;, width=.1, position=pd) +
  geom_line(position=pd) +
  geom_point(position=pd, size=3, shape=21, fill=&quot;white&quot;) + 
  xlab(&quot;&quot;) +
  ylab(&quot;&quot;) +
  scale_color_manual(values=c(&quot;Democrat&quot; = &quot;blue&quot;, 
                              &quot;Republican&quot; = &quot;red&quot;)) + 
  scale_x_discrete(breaks=c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;),
                   labels=c(&quot;Solid\nDemocrat&quot;, &quot;Solid\nRepublican&quot;)) + 
  ggtitle(&quot;District Attorney&quot;) +
  ylim(0,1) +                        
  theme_bw() 


# U.S. House
houseContrast &lt;- cces2.2
houseContrast &lt;- subset(daContrast, c(Pres_Vote == &quot;Solid Democrat&quot; | Pres_Vote == &quot;Solid Republican&quot;))

houseContrast$pid &lt;- factor(houseContrast$pid)
houseContrastD &lt;- subset(houseContrast, pid==&quot;Democrat&quot;)
houseContrastR &lt;- subset(houseContrast, pid==&quot;Republican&quot;)

houseContrastD &lt;- summarySE(houseContrastD, measurevar=&quot;RCO327_np&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
houseContrastD$office &lt;- &quot;u.s. house&quot;
houseContrastD$party &lt;- &quot;Democrat&quot;
houseContrastD$proportion &lt;- houseContrastD$RCO327_np
houseContrastD$RCO327_np &lt;- NULL

houseContrastR &lt;- summarySE(houseContrastR, measurevar=&quot;RCO327_np&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
houseContrastR$office &lt;- &quot;u.s. house&quot;
houseContrastR$party &lt;- &quot;Republican&quot;
houseContrastR$proportion &lt;- houseContrastR$RCO327_np
houseContrastR$RCO327_np &lt;- NULL

houseContrastDR &lt;- rbind(houseContrastD, houseContrastR)
houseContrastDR$Pres_Vote &lt;- ordered(houseContrastDR$Pres_Vote, 
                                     levels = c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;))

pd &lt;- position_dodge(0.1)
houseCD &lt;- ggplot(houseContrastDR, aes(x=Pres_Vote, y=proportion, colour=party, group=party, legend=&quot;right&quot;)) + 
  geom_errorbar(aes(ymin=proportion-ci, ymax=proportion+ci), colour=&quot;black&quot;, width=.1, position=pd) +
  geom_line(position=pd) +
  geom_point(position=pd, size=3, shape=21, fill=&quot;white&quot;) + 
  xlab(&quot;&quot;) +
  ylab(&quot;&quot;) +
  scale_color_manual(values=c(&quot;Democrat&quot; = &quot;blue&quot;, 
                              &quot;Republican&quot; = &quot;red&quot;)) +   
  scale_x_discrete(breaks=c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;),
                   labels=c(&quot;Solid\nDemocrat&quot;, &quot;Solid\nRepublican&quot;)) + 
  ggtitle(&quot;U.S. House&quot;) +
  ylim(0,1) +                       
  theme_bw() 



###### Multiplot for Nonpartisan #####

#extract legend from houseCD (could be any b/c all same legend)
leg &lt;- get_legend(houseCD)

#save earlier plots but remove their legends
sbCD &lt;- sbCD + theme(legend.position = &quot;none&quot;)
leoCD &lt;- leoCD + theme(legend.position = &quot;none&quot;)
sherCD &lt;- sherCD + theme(legend.position = &quot;none&quot;)
daCD &lt;- daCD + theme(legend.position = &quot;none&quot;)
houseCD &lt;- houseCD + theme(legend.position = &quot;none&quot;)

#arrange plots, and now the extracted legend is a defacto plot (for positioning)
multi_npContrast &lt;- ggarrange(sbCD, leoCD, sherCD,  daCD, houseCD, leg, 
                              ncol = 3, nrow = 2,
                              font.label=list(color=&quot;black&quot;,size=10))

annotate_figure(multi_npContrast,
                top = text_grob(&quot;&quot;, 
                                color = &quot;black&quot;, face = &quot;bold&quot;, size = 14),
                bottom = text_grob(&quot;2016 County Presidential Vote&quot;, 
                                   color = &quot;black&quot;,
                                   hjust = 0.35, vjust = -0.75, size = 12),
                left = text_grob(&quot;Proportion&quot;, color = &quot;black&quot;, rot = 90),
                right = &quot;&quot;,
                fig.lab = &quot;&quot;, fig.lab.face = &quot;bold&quot;
)</code></pre>
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" width="672" /></p>
<pre class="r"><code>##################################################################

# Table 2. Choosing &quot;Nonpartisan election&quot; as preferred selection method by county-level presidential vote #

##################################################################

# Inter-party Diffs, Stratified by County, Showing only highest-level factor coefs
# Re-Running Basic models and Full, Stratified by PresVote, export tables only showing highest level

Basic.inter.sb.SolidD &lt;- glm(RCO322_np ~ 
                               pid,
                             family=&quot;binomial&quot;,
                             data = subset(sbMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Finter.sb.SolidD &lt;- glm(RCO322_np ~ 
                          pid +
                          perceive_extreme_very.f +
                          polint.f +
                          educ.int.f +
                          birthyr,
                        family=&quot;binomial&quot;,
                        data = subset(sbMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Basic.inter.sb.LeanD &lt;- glm(RCO322_np ~ 
                              pid,
                            family=&quot;binomial&quot;,
                            data = subset(sbMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Finter.sb.LeanD &lt;- glm(RCO322_np ~ 
                         pid +
                         perceive_extreme_very.f +
                         polint.f +
                         educ.int.f +
                         birthyr,
                       family=&quot;binomial&quot;,
                       data = subset(sbMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Basic.inter.sb.LeanR &lt;- glm(RCO322_np ~ 
                              pid,
                            family=&quot;binomial&quot;,
                            data = subset(sbMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Finter.sb.LeanR &lt;- glm(RCO322_np ~ 
                         pid +
                         perceive_extreme_very.f +
                         polint.f +
                         educ.int.f +
                         birthyr,
                       family=&quot;binomial&quot;,
                       data = subset(sbMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Basic.inter.sb.SolidR &lt;- glm(RCO322_np ~ 
                               pid,
                             family=&quot;binomial&quot;,
                             data = subset(sbMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

Finter.sb.SolidR &lt;- glm(RCO322_np ~ 
                          pid +
                          perceive_extreme_very.f +
                          polint.f +
                          educ.int.f +
                          birthyr,
                        family=&quot;binomial&quot;,
                        data = subset(sbMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

pl &lt;- c(
  `pidRepublican` = &quot;Party ID&quot;,
  `polint.f4` = &quot;News: Most of the time&quot;,
  `educ.int.f5` = &quot;Education: Post-graduate&quot;,
  `perceive_extreme_very.f1` = &quot;Out-party is very extreme&quot;)

tab_model(Basic.inter.sb.SolidD, Finter.sb.SolidD, Basic.inter.sb.LeanD, Finter.sb.LeanD, 
          Basic.inter.sb.LeanR, Finter.sb.LeanR, Basic.inter.sb.SolidR, Finter.sb.SolidR, 
          terms=c(&quot;pidRepublican&quot;,
                  &quot;polint.f4&quot;,
                  &quot;educ.int.f5&quot;,
                  &quot;perceive_extreme_very.f1&quot;),
          dv.labels = c(&quot;Solid Democrat&quot;,&quot;&quot;,&quot;Lean Democrat&quot;,&quot;&quot;,
                        &quot;Lean Republican&quot;,&quot;&quot;,&quot;Solid Republican&quot;,&quot;&quot;),
          pred.labels = pl,
          collapse.se=TRUE,
          show.ci=FALSE,
          p.style=&quot;stars&quot;,
          transform=NULL,
          show.aic=TRUE)</code></pre>
<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">
 
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">
Predictors
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col7">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col8">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col9">
Log-Odds
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Party ID
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.31 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.22 <sup></sup><br>(0.29)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.31 <sup></sup><br>(0.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.54 <sup>**</sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.54 <sup>**</sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
-0.68 <sup>**</sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
-0.88 <sup>***</sup><br>(0.24)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
-1.03 <sup>***</sup><br>(0.26)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Out-party is very extreme
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.08 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.04 <sup></sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.26 <sup></sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
0.14 <sup></sup><br>(0.24)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Most of the time
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.59 <sup></sup><br>(0.52)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.62 <sup></sup><br>(0.56)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
1.74 <sup>**</sup><br>(0.60)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
1.32 <sup>*</sup><br>(0.58)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: Post-graduate
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.76 <sup>***</sup><br>(0.39)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.35 <sup></sup><br>(0.32)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.01 <sup></sup><br>(0.34)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
-0.12 <sup></sup><br>(0.40)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">
Observations
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
334
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
329
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
515
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
508
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
451
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
445
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
334
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
327
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
R<sup>2</sup> Tjur
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.004
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.143
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.006
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.081
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.018
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.083
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.042
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.099
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
AIC
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
452.466
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
415.858
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
703.503
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
670.327
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
608.668
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
588.983
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
450.809
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
438.261
</td>
</tr>
<tr>
<td colspan="9" style="font-style:italic; border-top:double black; text-align:right;">
<ul>
<li>p&lt;0.05   ** p&lt;0.01   *** p&lt;0.001
</td>
</tr></li>
</ul>
</table>
<pre class="r"><code># LEO #

Basic.inter.leo.SolidD &lt;- glm(RCO321_np ~ 
                                pid,
                              family=&quot;binomial&quot;,
                              data = subset(leoMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Finter.leo.SolidD &lt;- glm(RCO321_np ~ 
                           pid +
                           perceive_extreme_very.f +
                           polint.f +
                           educ.int.f +
                           birthyr,
                         family=&quot;binomial&quot;,
                         data = subset(leoMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Basic.inter.leo.LeanD &lt;- glm(RCO321_np ~ 
                               pid,
                             family=&quot;binomial&quot;,
                             data = subset(leoMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Finter.leo.LeanD &lt;- glm(RCO321_np ~ 
                          pid +
                          perceive_extreme_very.f +
                          polint.f +
                          educ.int.f +
                          birthyr,
                        family=&quot;binomial&quot;,
                        data = subset(leoMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Basic.inter.leo.LeanR &lt;- glm(RCO321_np ~ 
                               pid,
                             family=&quot;binomial&quot;,
                             data = subset(leoMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Finter.leo.LeanR &lt;- glm(RCO321_np ~ 
                          pid +
                          perceive_extreme_very.f +
                          polint.f +
                          educ.int.f +
                          birthyr,
                        family=&quot;binomial&quot;,
                        data = subset(leoMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Basic.inter.leo.SolidR &lt;- glm(RCO321_np ~ 
                                pid,
                              family=&quot;binomial&quot;,
                              data = subset(leoMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

Finter.leo.SolidR &lt;- glm(RCO321_np ~ 
                           pid +
                           perceive_extreme_very.f +
                           polint.f +
                           educ.int.f +
                           birthyr,
                         family=&quot;binomial&quot;,
                         data = subset(leoMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

pl &lt;- c(
  `pidRepublican` = &quot;Party ID&quot;,
  `polint.f4` = &quot;News: Most of the time&quot;,
  `educ.int.f5` = &quot;Education: Post-graduate&quot;,
  `perceive_extreme_very.f1` = &quot;Out-party is very extreme&quot;)

tab_model(Basic.inter.leo.SolidD, Finter.leo.SolidD, Basic.inter.leo.LeanD, Finter.leo.LeanD, 
          Basic.inter.leo.LeanR, Finter.leo.LeanR, Basic.inter.leo.SolidR, Finter.leo.SolidR, 
          terms=c(&quot;pidRepublican&quot;,
                  &quot;polint.f4&quot;,
                  &quot;educ.int.f5&quot;,
                  &quot;perceive_extreme_very.f1&quot;),
          dv.labels = c(&quot;Solid Democrat&quot;,&quot;&quot;,&quot;Lean Democrat&quot;,&quot;&quot;,
                        &quot;Lean Republican&quot;,&quot;&quot;,&quot;Solid Republican&quot;,&quot;&quot;),
          pred.labels = pl,
          collapse.se=TRUE,
          show.ci=FALSE,
          p.style=&quot;stars&quot;,
          transform=NULL,
          show.aic=TRUE)</code></pre>
<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">
 
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">
Predictors
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col7">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col8">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col9">
Log-Odds
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Party ID
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.44 <sup></sup><br>(0.25)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.55 <sup>*</sup><br>(0.27)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.11 <sup></sup><br>(0.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.01 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.74 <sup>***</sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
-0.80 <sup>***</sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
-0.81 <sup>***</sup><br>(0.23)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
-0.99 <sup>***</sup><br>(0.25)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Out-party is very extreme
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.20 <sup></sup><br>(0.24)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.39 <sup>*</sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.21 <sup></sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
0.41 <sup></sup><br>(0.24)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Most of the time
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.85 <sup></sup><br>(0.57)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.13 <sup></sup><br>(0.61)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
1.38 <sup>*</sup><br>(0.60)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
1.34 <sup>*</sup><br>(0.62)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: Post-graduate
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.51 <sup></sup><br>(0.34)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.07 <sup></sup><br>(0.30)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
-0.12 <sup></sup><br>(0.34)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
-0.42 <sup></sup><br>(0.40)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">
Observations
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
346
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
341
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
518
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
510
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
449
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
443
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
337
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
330
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
R<sup>2</sup> Tjur
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.009
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.062
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.001
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.031
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.033
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.065
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.038
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.081
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
AIC
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
477.975
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
470.758
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
720.603
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
712.251
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
608.819
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
603.787
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
453.998
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
448.427
</td>
</tr>
<tr>
<td colspan="9" style="font-style:italic; border-top:double black; text-align:right;">
<ul>
<li>p&lt;0.05   ** p&lt;0.01   *** p&lt;0.001
</td>
</tr></li>
</ul>
</table>
<pre class="r"><code># sheriff #

Basic.inter.sher.SolidD &lt;- glm(RCO323_np ~ 
                                 pid,
                               family=&quot;binomial&quot;,
                               data = subset(sherMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Finter.sher.SolidD &lt;- glm(RCO323_np ~ 
                            pid +
                            perceive_extreme_very.f +
                            polint.f +
                            educ.int.f +
                            birthyr,
                          family=&quot;binomial&quot;,
                          data = subset(sherMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Basic.inter.sher.LeanD &lt;- glm(RCO323_np ~ 
                                pid,
                              family=&quot;binomial&quot;,
                              data = subset(sherMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Finter.sher.LeanD &lt;- glm(RCO323_np ~ 
                           pid +
                           perceive_extreme_very.f +
                           polint.f +
                           educ.int.f +
                           birthyr,
                         family=&quot;binomial&quot;,
                         data = subset(sherMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Basic.inter.sher.LeanR &lt;- glm(RCO323_np ~ 
                                pid,
                              family=&quot;binomial&quot;,
                              data = subset(sherMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Finter.sher.LeanR &lt;- glm(RCO323_np ~ 
                           pid +
                           perceive_extreme_very.f +
                           polint.f +
                           educ.int.f +
                           birthyr,
                         family=&quot;binomial&quot;,
                         data = subset(sherMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Basic.inter.sher.SolidR &lt;- glm(RCO323_np ~ 
                                 pid,
                               family=&quot;binomial&quot;,
                               data = subset(sherMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

Finter.sher.SolidR &lt;- glm(RCO323_np ~ 
                            pid +
                            perceive_extreme_very.f +
                            polint.f +
                            educ.int.f +
                            birthyr,
                          family=&quot;binomial&quot;,
                          data = subset(sherMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

pl &lt;- c(
  `pidRepublican` = &quot;Party ID&quot;,
  `polint.f4` = &quot;News: Most of the time&quot;,
  `educ.int.f5` = &quot;Education: Post-graduate&quot;,
  `perceive_extreme_very.f1` = &quot;Out-party is very extreme&quot;)

tab_model(Basic.inter.sher.SolidD, Finter.sher.SolidD, Basic.inter.sher.LeanD, Finter.sher.LeanD, 
          Basic.inter.sher.LeanR, Finter.sher.LeanR, Basic.inter.sher.SolidR, Finter.sher.SolidR, 
          terms=c(&quot;pidRepublican&quot;,
                  &quot;polint.f4&quot;,
                  &quot;educ.int.f5&quot;,
                  &quot;perceive_extreme_very.f1&quot;),
          dv.labels = c(&quot;Solid Democrat&quot;,&quot;&quot;,&quot;Lean Democrat&quot;,&quot;&quot;,
                        &quot;Lean Republican&quot;,&quot;&quot;,&quot;Solid Republican&quot;,&quot;&quot;),
          pred.labels = pl,
          collapse.se=TRUE,
          show.ci=FALSE,
          p.style=&quot;stars&quot;,
          transform=NULL,
          show.aic=TRUE)</code></pre>
<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">
 
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">
Predictors
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col7">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col8">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col9">
Log-Odds
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Party ID
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.27 <sup></sup><br>(0.24)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.22 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.10 <sup></sup><br>(0.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.03 <sup></sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.53 <sup>**</sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
-0.59 <sup>**</sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
-0.82 <sup>***</sup><br>(0.23)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
-0.96 <sup>***</sup><br>(0.25)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Out-party is very extreme
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.02 <sup></sup><br>(0.24)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.15 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.10 <sup></sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
0.18 <sup></sup><br>(0.24)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Most of the time
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.63 <sup></sup><br>(0.53)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.58 <sup></sup><br>(0.56)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.84 <sup></sup><br>(0.54)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
1.57 <sup>**</sup><br>(0.59)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: Post-graduate
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.70 <sup>*</sup><br>(0.34)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.20 <sup></sup><br>(0.30)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.08 <sup></sup><br>(0.33)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
-0.65 <sup></sup><br>(0.39)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">
Observations
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
347
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
342
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
514
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
506
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
452
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
446
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
338
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
331
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
R<sup>2</sup> Tjur
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.003
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.061
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.001
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.043
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.017
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.047
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.037
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.097
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
AIC
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
482.793
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
473.817
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
713.752
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
698.025
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
622.246
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
618.267
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
459.209
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
445.604
</td>
</tr>
<tr>
<td colspan="9" style="font-style:italic; border-top:double black; text-align:right;">
<ul>
<li>p&lt;0.05   ** p&lt;0.01   *** p&lt;0.001
</td>
</tr></li>
</ul>
</table>
<pre class="r"><code># d.a. #

Basic.inter.da.SolidD &lt;- glm(RCO324_np ~ 
                               pid,
                             family=&quot;binomial&quot;,
                             data = subset(daMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Finter.da.SolidD &lt;- glm(RCO324_np ~ 
                          pid +
                          perceive_extreme_very.f +
                          polint.f +
                          educ.int.f +
                          birthyr,
                        family=&quot;binomial&quot;,
                        data = subset(daMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Basic.inter.da.LeanD &lt;- glm(RCO324_np ~ 
                              pid,
                            family=&quot;binomial&quot;,
                            data = subset(daMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Finter.da.LeanD &lt;- glm(RCO324_np ~ 
                         pid +
                         perceive_extreme_very.f +
                         polint.f +
                         educ.int.f +
                         birthyr,
                       family=&quot;binomial&quot;,
                       data = subset(daMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Basic.inter.da.LeanR &lt;- glm(RCO324_np ~ 
                              pid,
                            family=&quot;binomial&quot;,
                            data = subset(daMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Finter.da.LeanR &lt;- glm(RCO324_np ~ 
                         pid +
                         perceive_extreme_very.f +
                         polint.f +
                         educ.int.f +
                         birthyr,
                       family=&quot;binomial&quot;,
                       data = subset(daMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Basic.inter.da.SolidR &lt;- glm(RCO324_np ~ 
                               pid,
                             family=&quot;binomial&quot;,
                             data = subset(daMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

Finter.da.SolidR &lt;- glm(RCO324_np ~ 
                          pid +
                          perceive_extreme_very.f +
                          polint.f +
                          educ.int.f +
                          birthyr,
                        family=&quot;binomial&quot;,
                        data = subset(daMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

pl &lt;- c(
  `pidRepublican` = &quot;Party ID&quot;,
  `polint.f4` = &quot;News: Most of the time&quot;,
  `educ.int.f5` = &quot;Education: Post-graduate&quot;,
  `perceive_extreme_very.f1` = &quot;Out-party is very extreme&quot;)

tab_model(Basic.inter.da.SolidD, Finter.da.SolidD, Basic.inter.da.LeanD, Finter.da.LeanD, 
          Basic.inter.da.LeanR, Finter.da.LeanR, Basic.inter.da.SolidR, Finter.da.SolidR, 
          terms=c(&quot;pidRepublican&quot;,
                  &quot;polint.f4&quot;,
                  &quot;educ.int.f5&quot;,
                  &quot;perceive_extreme_very.f1&quot;),
          dv.labels = c(&quot;Solid Democrat&quot;,&quot;&quot;,&quot;Lean Democrat&quot;,&quot;&quot;,
                        &quot;Lean Republican&quot;,&quot;&quot;,&quot;Solid Republican&quot;,&quot;&quot;),
          pred.labels = pl,
          collapse.se=TRUE,
          show.ci=FALSE,
          p.style=&quot;stars&quot;,
          transform=NULL,
          show.aic=TRUE)</code></pre>
<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">
 
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">
Predictors
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col7">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col8">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col9">
Log-Odds
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Party ID
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.16 <sup></sup><br>(0.24)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.09 <sup></sup><br>(0.27)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.21 <sup></sup><br>(0.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.10 <sup></sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.60 <sup>**</sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
-0.77 <sup>***</sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
-0.62 <sup>**</sup><br>(0.23)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
-0.88 <sup>***</sup><br>(0.25)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Out-party is very extreme
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.16 <sup></sup><br>(0.25)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.11 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.21 <sup></sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
0.24 <sup></sup><br>(0.24)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Most of the time
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.19 <sup></sup><br>(0.52)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.50 <sup>*</sup><br>(0.67)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
1.01 <sup></sup><br>(0.57)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
2.77 <sup>***</sup><br>(0.79)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: Post-graduate
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.25 <sup>***</sup><br>(0.36)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.29 <sup></sup><br>(0.30)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.03 <sup></sup><br>(0.34)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
-0.43 <sup></sup><br>(0.39)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">
Observations
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
346
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
341
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
515
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
507
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
452
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
446
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
334
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
327
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
R<sup>2</sup> Tjur
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.001
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.123
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.003
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.041
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.022
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.063
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.022
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.108
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
AIC
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
483.115
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
450.511
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
716.605
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
703.438
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
620.238
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
611.336
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
459.399
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
437.536
</td>
</tr>
<tr>
<td colspan="9" style="font-style:italic; border-top:double black; text-align:right;">
<ul>
<li>p&lt;0.05   ** p&lt;0.01   *** p&lt;0.001
</td>
</tr></li>
</ul>
</table>
<pre class="r"><code># House #

Basic.inter.house.SolidD &lt;- glm(RCO327_np ~ 
                                  pid,
                                family=&quot;binomial&quot;,
                                data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Finter.house.SolidD &lt;- glm(RCO327_np ~ 
                             pid +
                             perceive_extreme_very.f +
                             polint.f +
                             educ.int.f +
                             birthyr,
                           family=&quot;binomial&quot;,
                           data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Basic.inter.house.LeanD &lt;- glm(RCO327_np ~ 
                                 pid,
                               family=&quot;binomial&quot;,
                               data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Finter.house.LeanD &lt;- glm(RCO327_np ~ 
                            pid +
                            perceive_extreme_very.f +
                            polint.f +
                            educ.int.f +
                            birthyr,
                          family=&quot;binomial&quot;,
                          data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Basic.inter.house.LeanR &lt;- glm(RCO327_np ~ 
                                 pid,
                               family=&quot;binomial&quot;,
                               data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Finter.house.LeanR &lt;- glm(RCO327_np ~ 
                            pid +
                            perceive_extreme_very.f +
                            polint.f +
                            educ.int.f +
                            birthyr,
                          family=&quot;binomial&quot;,
                          data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Basic.inter.house.SolidR &lt;- glm(RCO327_np ~ 
                                  pid,
                                family=&quot;binomial&quot;,
                                data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

Finter.house.SolidR &lt;- glm(RCO327_np ~ 
                             pid +
                             perceive_extreme_very.f +
                             polint.f +
                             educ.int.f +
                             birthyr,
                           family=&quot;binomial&quot;,
                           data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

pl &lt;- c(
  `pidRepublican` = &quot;Party ID&quot;,
  `polint.f4` = &quot;News: Most of the time&quot;,
  `educ.int.f5` = &quot;Education: Post-graduate&quot;,
  `perceive_extreme_very.f1` = &quot;Out-party is very extreme&quot;)

tab_model(Basic.inter.house.SolidD, Finter.house.SolidD, Basic.inter.house.LeanD, Finter.house.LeanD, 
          Basic.inter.house.LeanR, Finter.house.LeanR, Basic.inter.house.SolidR, Finter.house.SolidR, 
          terms=c(&quot;pidRepublican&quot;,
                  &quot;polint.f4&quot;,
                  &quot;educ.int.f5&quot;,
                  &quot;perceive_extreme_very.f1&quot;),
          dv.labels = c(&quot;Solid Democrat&quot;,&quot;&quot;,&quot;Lean Democrat&quot;,&quot;&quot;,
                        &quot;Lean Republican&quot;,&quot;&quot;,&quot;Solid Republican&quot;,&quot;&quot;),
          pred.labels = pl,
          collapse.se=TRUE,
          show.ci=FALSE,
          p.style=&quot;stars&quot;,
          transform=NULL,
          show.aic=TRUE)</code></pre>
<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">
 
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">
Predictors
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col7">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col8">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col9">
Log-Odds
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Party ID
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.43 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.43 <sup></sup><br>(0.27)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.33 <sup></sup><br>(0.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.22 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.52 <sup>**</sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
-0.60 <sup>**</sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
-0.64 <sup>**</sup><br>(0.23)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
-0.78 <sup>**</sup><br>(0.25)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Out-party is very extreme
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.20 <sup></sup><br>(0.25)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.16 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
-0.18 <sup></sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
0.45 <sup></sup><br>(0.24)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Most of the time
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.10 <sup></sup><br>(0.52)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.06 <sup></sup><br>(0.56)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.55 <sup></sup><br>(0.57)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
1.38 <sup>*</sup><br>(0.69)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: Post-graduate
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.50 <sup></sup><br>(0.35)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.35 <sup></sup><br>(0.31)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
-0.88 <sup>*</sup><br>(0.40)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
-0.64 <sup></sup><br>(0.40)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">
Observations
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
342
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
337
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
518
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
510
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
450
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
444
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
337
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
330
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
R<sup>2</sup> Tjur
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.008
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.030
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.006
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.025
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.015
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.060
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.023
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.083
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
AIC
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
455.445
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
461.097
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
704.502
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
703.667
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
584.108
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
572.597
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
445.764
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
435.151
</td>
</tr>
<tr>
<td colspan="9" style="font-style:italic; border-top:double black; text-align:right;">
<ul>
<li>p&lt;0.05   ** p&lt;0.01   *** p&lt;0.001
</td>
</tr></li>
</ul>
</table>
<pre class="r"><code>##################################################################

### Figure 4. Inter-party differences in predicted probability of choosing nonpartisan elections.

##################################################################

Finter.sb.SolidD &lt;- glm(RCO322_np ~ 
                          pid +
                          perceive_extreme_very.f +
                          polint.f +
                          educ.int.f +
                          birthyr,
                        family=&quot;binomial&quot;,
                        data = subset(sbMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))
#
Finter.sb.LeanD &lt;- glm(RCO322_np ~ 
                         pid +
                         perceive_extreme_very.f +
                         polint.f +
                         educ.int.f +
                         birthyr,
                       family=&quot;binomial&quot;,
                       data = subset(sbMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))
#
Finter.sb.LeanR &lt;- glm(RCO322_np ~ 
                         pid +
                         perceive_extreme_very.f +
                         polint.f +
                         educ.int.f +
                         birthyr,
                       family=&quot;binomial&quot;,
                       data = subset(sbMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))
#
Finter.sb.SolidR &lt;- glm(RCO322_np ~ 
                          pid +
                          perceive_extreme_very.f +
                          polint.f +
                          educ.int.f +
                          birthyr,
                        family=&quot;binomial&quot;,
                        data = subset(sbMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

# Using margins() we calculate the marginal effects for each variable:

sbInter.mSolidD &lt;- margins(Finter.sb.SolidD)
sbInter.mLeanD &lt;- margins(Finter.sb.LeanD)
sbInter.mLeanR &lt;- margins(Finter.sb.LeanR)
sbInter.mSolidR &lt;- margins(Finter.sb.SolidR)

#we can take results from margins() and plot them ourselves. 
#To clean up the summary a little a little, we convert it to a tibble, 
#then use prefix_strip() and prefix_replace() to tidy the labels. 

bo_gg_SolidD &lt;- as_tibble(summary(sbInter.mSolidD))
bo_gg_LeanD &lt;- as_tibble(summary(sbInter.mLeanD))
bo_gg_LeanR &lt;- as_tibble(summary(sbInter.mLeanR))
bo_gg_SolidR &lt;- as_tibble(summary(sbInter.mSolidR))

bo_gg_SolidD %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower    upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                  -0.00276 -0.00579 0.000274
##  2 educ.int.f2               0.142   -0.0328  0.317   
##  3 educ.int.f3               0.119   -0.0782  0.317   
##  4 educ.int.f4               0.254    0.0981  0.410   
##  5 educ.int.f5               0.379    0.229   0.528   
##  6 perceive_extreme_very.f1  0.0169  -0.0876  0.121   
##  7 pidRepublican             0.0444  -0.0695  0.158   
##  8 polint.f2                -0.0136  -0.276   0.249   
##  9 polint.f3                 0.0254  -0.206   0.257   
## 10 polint.f4                 0.128   -0.0984  0.354</code></pre>
<pre class="r"><code>bo_gg_LeanD %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower     upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;     &lt;dbl&gt;
##  1 birthyr                  -0.00339 -0.00587 -0.000902
##  2 educ.int.f2              -0.0390  -0.160    0.0822  
##  3 educ.int.f3               0.0754  -0.0893   0.240   
##  4 educ.int.f4               0.0481  -0.0688   0.165   
##  5 educ.int.f5               0.0789  -0.0586   0.217   
##  6 perceive_extreme_very.f1  0.00913 -0.0771   0.0954  
##  7 pidRepublican            -0.121   -0.209   -0.0317  
##  8 polint.f2                -0.0969  -0.372    0.178   
##  9 polint.f3                 0.00885 -0.253    0.271   
## 10 polint.f4                 0.144   -0.118    0.406</code></pre>
<pre class="r"><code>bo_gg_LeanR %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower     upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;     &lt;dbl&gt;
##  1 birthyr                  -0.00196 -0.00466  0.000741
##  2 educ.int.f2               0.0954  -0.0297   0.221   
##  3 educ.int.f3               0.0384  -0.118    0.195   
##  4 educ.int.f4               0.0268  -0.0987   0.152   
##  5 educ.int.f5               0.00163 -0.153    0.156   
##  6 perceive_extreme_very.f1  0.0580  -0.0327   0.149   
##  7 pidRepublican            -0.153   -0.243   -0.0622  
##  8 polint.f2                 0.131   -0.124    0.386   
##  9 polint.f3                 0.346    0.124    0.568   
## 10 polint.f4                 0.391    0.175    0.607</code></pre>
<pre class="r"><code>bo_gg_SolidR %&gt;% select(factor, AME, lower, upper)</code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower    upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                  -0.00118 -0.00442  0.00206
##  2 educ.int.f2               0.0960  -0.0392   0.231  
##  3 educ.int.f3              -0.124   -0.299    0.0503 
##  4 educ.int.f4               0.0450  -0.107    0.197  
##  5 educ.int.f5              -0.0261  -0.203    0.151  
##  6 perceive_extreme_very.f1  0.0318  -0.0723   0.136  
##  7 pidRepublican            -0.231   -0.336   -0.126  
##  8 polint.f2                 0.117   -0.143    0.377  
##  9 polint.f3                 0.146   -0.0943   0.387  
## 10 polint.f4                 0.297    0.0625   0.531</code></pre>
<pre class="r"><code>## Subset further to plot only certain factors ##

bo_gg_SolidD_2 &lt;- subset(bo_gg_SolidD, 
                         factor==&quot;pidRepublican&quot;)
bo_gg_SolidD_2$County = &quot;Solid Democrat&quot;
bo_gg_SolidD_2$factor &lt;- as.factor(bo_gg_SolidD_2$factor)
bo_gg_SolidD_2$var1 &lt;- &quot;Republican&quot;

bo_gg_LeanD_2 &lt;- subset(bo_gg_LeanD, 
                        factor==&quot;pidRepublican&quot;)
bo_gg_LeanD_2$County = &quot;Lean Democrat&quot;
bo_gg_LeanD_2$factor &lt;- as.factor(bo_gg_LeanD_2$factor)
bo_gg_LeanD_2$var1 &lt;- &quot;Republican&quot;

bo_gg_LeanR_2 &lt;- subset(bo_gg_LeanR, 
                        factor==&quot;pidRepublican&quot;)
bo_gg_LeanR_2$County = &quot;Lean Republican&quot;
bo_gg_LeanR_2$factor &lt;- as.factor(bo_gg_LeanR_2$factor)
bo_gg_LeanR_2$var1 &lt;- &quot;Republican&quot;

bo_gg_SolidR_2 &lt;- subset(bo_gg_SolidR, 
                         factor==&quot;pidRepublican&quot;)
bo_gg_SolidR_2$County = &quot;Solid Republican&quot;
bo_gg_SolidR_2$factor &lt;- as.factor(bo_gg_SolidR_2$factor)
bo_gg_SolidR_2$var1 &lt;- &quot;Republican&quot;


# Combine Plots #
pd &lt;- position_dodge(0.25)
bo_gg_SB_Combine &lt;- rbind(bo_gg_SolidD_2, bo_gg_LeanD_2, bo_gg_LeanR_2, bo_gg_SolidR_2)
bo_gg_SB_Combine$Office &lt;- &quot;School Board&quot;

ord &lt;- c(&quot;Solid Democrat&quot;,
         &quot;Lean Democrat&quot;,
         &quot;Lean Republican&quot;,
         &quot;Solid Republican&quot;)

# Correct Order for Plotting
bo_gg_SB_Combine$County &lt;- factor(bo_gg_SB_Combine$County,levels=ord)

## NOW: Repeat above for each office, then RBIND into one big dataframe

Finter.leo.SolidD &lt;- glm(RCO321_np ~ 
                           pid +
                           perceive_extreme_very.f +
                           polint.f +
                           educ.int.f +
                           birthyr,
                         family=&quot;binomial&quot;,
                         data = subset(leoMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Finter.leo.LeanD &lt;- glm(RCO321_np ~ 
                          pid +
                          perceive_extreme_very.f +
                          polint.f +
                          educ.int.f +
                          birthyr,
                        family=&quot;binomial&quot;,
                        data = subset(leoMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Finter.leo.LeanR &lt;- glm(RCO321_np ~ 
                          pid +
                          perceive_extreme_very.f +
                          polint.f +
                          educ.int.f +
                          birthyr,
                        family=&quot;binomial&quot;,
                        data = subset(leoMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Finter.leo.SolidR &lt;- glm(RCO321_np ~ 
                           pid +
                           perceive_extreme_very.f +
                           polint.f +
                           educ.int.f +
                           birthyr,
                         family=&quot;binomial&quot;,
                         data = subset(leoMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

# Using margins() we calculate the marginal effects for each variable:

leoInter.mSolidD &lt;- margins(Finter.leo.SolidD)
leoInter.mLeanD &lt;- margins(Finter.leo.LeanD)
leoInter.mLeanR &lt;- margins(Finter.leo.LeanR)
leoInter.mSolidR &lt;- margins(Finter.leo.SolidR)

#we can take results from margins() and plot them ourselves. 
#To clean up the summary a little a little, we convert it to a tibble, 
#then use prefix_strip() and prefix_replace() to tidy the labels. 

LEObo_gg_SolidD &lt;- as_tibble(summary(leoInter.mSolidD))
LEObo_gg_LeanD &lt;- as_tibble(summary(leoInter.mLeanD))
LEObo_gg_LeanR &lt;- as_tibble(summary(leoInter.mLeanR))
LEObo_gg_SolidR &lt;- as_tibble(summary(leoInter.mSolidR))

LEObo_gg_SolidD %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower    upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                  -0.00435 -0.00750 -0.00120
##  2 educ.int.f2               0.00638 -0.164    0.177  
##  3 educ.int.f3              -0.0534  -0.243    0.136  
##  4 educ.int.f4              -0.0147  -0.170    0.141  
##  5 educ.int.f5               0.120   -0.0367   0.277  
##  6 perceive_extreme_very.f1  0.0466  -0.0631   0.156  
##  7 pidRepublican            -0.128   -0.246   -0.00875
##  8 polint.f2                 0.287    0.0293   0.544  
##  9 polint.f3                 0.168   -0.0553   0.392  
## 10 polint.f4                 0.187   -0.0335   0.407</code></pre>
<pre class="r"><code>LEObo_gg_LeanD %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                         AME    lower    upper
##    &lt;chr&gt;                        &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                  -0.000831 -0.00346  0.00179
##  2 educ.int.f2               0.0317   -0.0936   0.157  
##  3 educ.int.f3               0.144    -0.0240   0.312  
##  4 educ.int.f4              -0.0353   -0.157    0.0866 
##  5 educ.int.f5              -0.0161   -0.158    0.126  
##  6 perceive_extreme_very.f1 -0.0941   -0.183   -0.00549
##  7 pidRepublican             0.00177  -0.0904   0.0939 
##  8 polint.f2                 0.127    -0.121    0.375  
##  9 polint.f3                 0.212    -0.0229   0.446  
## 10 polint.f4                 0.257     0.0237   0.490</code></pre>
<pre class="r"><code>LEObo_gg_LeanR %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                         AME    lower    upper
##    &lt;chr&gt;                        &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                   0.000780 -0.00200  0.00356
##  2 educ.int.f2               0.0318   -0.0987   0.162  
##  3 educ.int.f3              -0.0643   -0.221    0.0929 
##  4 educ.int.f4               0.0257   -0.102    0.153  
##  5 educ.int.f5              -0.0284   -0.182    0.125  
##  6 perceive_extreme_very.f1  0.0497   -0.0425   0.142  
##  7 pidRepublican            -0.188    -0.281   -0.0959 
##  8 polint.f2                 0.0810   -0.150    0.312  
##  9 polint.f3                 0.250     0.0460   0.455  
## 10 polint.f4                 0.293     0.0950   0.490</code></pre>
<pre class="r"><code>LEObo_gg_SolidR %&gt;% select(factor, AME, lower, upper)</code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower    upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                  -0.00205 -0.00527  0.00117
##  2 educ.int.f2               0.0356  -0.104    0.175  
##  3 educ.int.f3              -0.00765 -0.185    0.170  
##  4 educ.int.f4              -0.00831 -0.161    0.145  
##  5 educ.int.f5              -0.0926  -0.261    0.0755 
##  6 perceive_extreme_very.f1  0.0936  -0.0113   0.199  
##  7 pidRepublican            -0.231   -0.340   -0.123  
##  8 polint.f2                 0.318    0.0901   0.545  
##  9 polint.f3                 0.218    0.0132   0.423  
## 10 polint.f4                 0.271    0.0752   0.467</code></pre>
<pre class="r"><code>## Subset further to plot only certain factors ##

LEObo_gg_SolidD_2 &lt;- subset(LEObo_gg_SolidD, 
                            factor==&quot;pidRepublican&quot;)
LEObo_gg_SolidD_2$County = &quot;Solid Democrat&quot;
LEObo_gg_SolidD_2$factor &lt;- as.factor(LEObo_gg_SolidD_2$factor)
LEObo_gg_SolidD_2$var1 &lt;- &quot;Republican&quot;

LEObo_gg_LeanD_2 &lt;- subset(LEObo_gg_LeanD, 
                           factor==&quot;pidRepublican&quot;)
LEObo_gg_LeanD_2$County = &quot;Lean Democrat&quot;
LEObo_gg_LeanD_2$factor &lt;- as.factor(LEObo_gg_LeanD_2$factor)
LEObo_gg_LeanD_2$var1 &lt;- &quot;Republican&quot;

LEObo_gg_LeanR_2 &lt;- subset(LEObo_gg_LeanR, 
                           factor==&quot;pidRepublican&quot;)
LEObo_gg_LeanR_2$County = &quot;Lean Republican&quot;
LEObo_gg_LeanR_2$factor &lt;- as.factor(LEObo_gg_LeanR_2$factor)
LEObo_gg_LeanR_2$var1 &lt;- &quot;Republican&quot;

LEObo_gg_SolidR_2 &lt;- subset(LEObo_gg_SolidR, 
                            factor==&quot;pidRepublican&quot;)
LEObo_gg_SolidR_2$County = &quot;Solid Republican&quot;
LEObo_gg_SolidR_2$factor &lt;- as.factor(LEObo_gg_SolidR_2$factor)
LEObo_gg_SolidR_2$var1 &lt;- &quot;Republican&quot;

bo_gg_LEO_Combine &lt;- rbind(LEObo_gg_SolidD_2, LEObo_gg_LeanD_2, LEObo_gg_LeanR_2, LEObo_gg_SolidR_2)
bo_gg_LEO_Combine$Office &lt;- &quot;Local Election Official&quot;

############## ########### ###########

Finter.sher.SolidD &lt;- glm(RCO323_np ~ 
                            pid +
                            perceive_extreme_very.f +
                            polint.f +
                            educ.int.f +
                            birthyr,
                          family=&quot;binomial&quot;,
                          data = subset(sherMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Finter.sher.LeanD &lt;- glm(RCO323_np ~ 
                           pid +
                           perceive_extreme_very.f +
                           polint.f +
                           educ.int.f +
                           birthyr,
                         family=&quot;binomial&quot;,
                         data = subset(sherMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Finter.sher.LeanR &lt;- glm(RCO323_np ~ 
                           pid +
                           perceive_extreme_very.f +
                           polint.f +
                           educ.int.f +
                           birthyr,
                         family=&quot;binomial&quot;,
                         data = subset(sherMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Finter.sher.SolidR &lt;- glm(RCO323_np ~ 
                            pid +
                            perceive_extreme_very.f +
                            polint.f +
                            educ.int.f +
                            birthyr,
                          family=&quot;binomial&quot;,
                          data = subset(sherMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

# Using margins() we calculate the marginal effects for each variable:

sherInter.mSolidD &lt;- margins(Finter.sher.SolidD)
sherInter.mLeanD &lt;- margins(Finter.sher.LeanD)
sherInter.mLeanR &lt;- margins(Finter.sher.LeanR)
sherInter.mSolidR &lt;- margins(Finter.sher.SolidR)

#we can take results from margins() and plot them ourselves. 
#To clean up the summary a little a little, we convert it to a tibble, 
#then use prefix_strip() and prefix_replace() to tidy the labels. 

sherbo_gg_SolidD &lt;- as_tibble(summary(sherInter.mSolidD))
sherbo_gg_LeanD &lt;- as_tibble(summary(sherInter.mLeanD))
sherbo_gg_LeanR &lt;- as_tibble(summary(sherInter.mLeanR))
sherbo_gg_SolidR &lt;- as_tibble(summary(sherInter.mSolidR))

sherbo_gg_SolidD %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower     upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;     &lt;dbl&gt;
##  1 birthyr                  -0.00348 -0.00665 -0.000301
##  2 educ.int.f2              -0.0949  -0.260    0.0700  
##  3 educ.int.f3               0.0638  -0.130    0.257   
##  4 educ.int.f4               0.0739  -0.0820   0.230   
##  5 educ.int.f5               0.168    0.0111   0.325   
##  6 perceive_extreme_very.f1  0.00491 -0.105    0.115   
##  7 pidRepublican             0.0510  -0.0702   0.172   
##  8 polint.f2                 0.163   -0.104    0.429   
##  9 polint.f3                 0.110   -0.121    0.342   
## 10 polint.f4                 0.144   -0.0854   0.373</code></pre>
<pre class="r"><code>sherbo_gg_LeanD %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower    upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                  -0.00176 -0.00437 0.000838
##  2 educ.int.f2              -0.100   -0.225   0.0248  
##  3 educ.int.f3               0.0911  -0.0764  0.259   
##  4 educ.int.f4               0.00418 -0.117   0.126   
##  5 educ.int.f5               0.0474  -0.0943  0.189   
##  6 perceive_extreme_very.f1  0.0363  -0.0526  0.125   
##  7 pidRepublican            -0.00814 -0.100   0.0838  
##  8 polint.f2                -0.0155  -0.296   0.265   
##  9 polint.f3                 0.0532  -0.214   0.321   
## 10 polint.f4                 0.141   -0.126   0.408</code></pre>
<pre class="r"><code>sherbo_gg_LeanR %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower    upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                  -0.00152 -0.00431  0.00127
##  2 educ.int.f2               0.0782  -0.0529   0.209  
##  3 educ.int.f3               0.0656  -0.0960   0.227  
##  4 educ.int.f4               0.0983  -0.0297   0.226  
##  5 educ.int.f5               0.0194  -0.138    0.177  
##  6 perceive_extreme_very.f1  0.0244  -0.0687   0.118  
##  7 pidRepublican            -0.142   -0.235   -0.0481 
##  8 polint.f2                 0.0190  -0.250    0.288  
##  9 polint.f3                 0.172   -0.0679   0.412  
## 10 polint.f4                 0.199   -0.0360   0.434</code></pre>
<pre class="r"><code>sherbo_gg_SolidR %&gt;% select(factor, AME, lower, upper)</code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower      upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;      &lt;dbl&gt;
##  1 birthyr                  -0.00321 -0.00636 -0.0000524
##  2 educ.int.f2              -0.0454  -0.183    0.0921   
##  3 educ.int.f3              -0.102   -0.279    0.0760   
##  4 educ.int.f4              -0.0653  -0.217    0.0862   
##  5 educ.int.f5              -0.145   -0.315    0.0249   
##  6 perceive_extreme_very.f1  0.0408  -0.0632   0.145    
##  7 pidRepublican            -0.218   -0.323   -0.113    
##  8 polint.f2                 0.299    0.0618   0.537    
##  9 polint.f3                 0.223    0.00665  0.439    
## 10 polint.f4                 0.339    0.131    0.547</code></pre>
<pre class="r"><code>## Subset further to plot only certain factors ##

sherbo_gg_SolidD_2 &lt;- subset(sherbo_gg_SolidD, 
                             factor==&quot;pidRepublican&quot;)
sherbo_gg_SolidD_2$County = &quot;Solid Democrat&quot;
sherbo_gg_SolidD_2$factor &lt;- as.factor(sherbo_gg_SolidD_2$factor)
sherbo_gg_SolidD_2$var1 &lt;- &quot;Republican&quot;

sherbo_gg_LeanD_2 &lt;- subset(sherbo_gg_LeanD, 
                            factor==&quot;pidRepublican&quot;)
sherbo_gg_LeanD_2$County = &quot;Lean Democrat&quot;
sherbo_gg_LeanD_2$factor &lt;- as.factor(sherbo_gg_LeanD_2$factor)
sherbo_gg_LeanD_2$var1 &lt;- &quot;Republican&quot;

sherbo_gg_LeanR_2 &lt;- subset(sherbo_gg_LeanR, 
                            factor==&quot;pidRepublican&quot;)
sherbo_gg_LeanR_2$County = &quot;Lean Republican&quot;
sherbo_gg_LeanR_2$factor &lt;- as.factor(sherbo_gg_LeanR_2$factor)
sherbo_gg_LeanR_2$var1 &lt;- &quot;Republican&quot;

sherbo_gg_SolidR_2 &lt;- subset(sherbo_gg_SolidR, 
                             factor==&quot;pidRepublican&quot;)
sherbo_gg_SolidR_2$County = &quot;Solid Republican&quot;
sherbo_gg_SolidR_2$factor &lt;- as.factor(sherbo_gg_SolidR_2$factor)
sherbo_gg_SolidR_2$var1 &lt;- &quot;Republican&quot;

bo_gg_sher_Combine &lt;- rbind(sherbo_gg_SolidD_2, sherbo_gg_LeanD_2, sherbo_gg_LeanR_2, sherbo_gg_SolidR_2)
bo_gg_sher_Combine$Office &lt;- &quot;Sheriff&quot;

############## ########### ###########

Finter.da.SolidD &lt;- glm(RCO324_np ~ 
                          pid +
                          perceive_extreme_very.f +
                          polint.f +
                          educ.int.f +
                          birthyr,
                        family=&quot;binomial&quot;,
                        data = subset(daMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Finter.da.LeanD &lt;- glm(RCO324_np ~ 
                         pid +
                         perceive_extreme_very.f +
                         polint.f +
                         educ.int.f +
                         birthyr,
                       family=&quot;binomial&quot;,
                       data = subset(daMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Finter.da.LeanR &lt;- glm(RCO324_np ~ 
                         pid +
                         perceive_extreme_very.f +
                         polint.f +
                         educ.int.f +
                         birthyr,
                       family=&quot;binomial&quot;,
                       data = subset(daMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Finter.da.SolidR &lt;- glm(RCO324_np ~ 
                          pid +
                          perceive_extreme_very.f +
                          polint.f +
                          educ.int.f +
                          birthyr,
                        family=&quot;binomial&quot;,
                        data = subset(daMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

# Using margins() we calculate the marginal effects for each variable:

daInter.mSolidD &lt;- margins(Finter.da.SolidD)
daInter.mLeanD &lt;- margins(Finter.da.LeanD)
daInter.mLeanR &lt;- margins(Finter.da.LeanR)
daInter.mSolidR &lt;- margins(Finter.da.SolidR)

#we can take results from margins() and plot them ourselves. 
#To clean up the summary a little a little, we convert it to a tibble, 
#then use prefix_strip() and prefix_replace() to tidy the labels. 

dabo_gg_SolidD &lt;- as_tibble(summary(daInter.mSolidD))
dabo_gg_LeanD &lt;- as_tibble(summary(daInter.mLeanD))
dabo_gg_LeanR &lt;- as_tibble(summary(daInter.mLeanR))
dabo_gg_SolidR &lt;- as_tibble(summary(daInter.mSolidR))

dabo_gg_SolidD %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower    upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                  -0.00517 -0.00819 -0.00215
##  2 educ.int.f2              -0.104   -0.268    0.0602 
##  3 educ.int.f3               0.0807  -0.111    0.272  
##  4 educ.int.f4               0.123   -0.0320   0.278  
##  5 educ.int.f5               0.285    0.134    0.436  
##  6 perceive_extreme_very.f1  0.0359  -0.0712   0.143  
##  7 pidRepublican             0.0190  -0.0990   0.137  
##  8 polint.f2                -0.00571 -0.267    0.256  
##  9 polint.f3                 0.0245  -0.202    0.251  
## 10 polint.f4                 0.0413  -0.183    0.266</code></pre>
<pre class="r"><code>dabo_gg_LeanD %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower   upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;
##  1 birthyr                  -0.00160 -0.00422 0.00102
##  2 educ.int.f2              -0.0141  -0.140   0.112  
##  3 educ.int.f3               0.0794  -0.0895  0.248  
##  4 educ.int.f4               0.0165  -0.105   0.138  
##  5 educ.int.f5               0.0702  -0.0716  0.212  
##  6 perceive_extreme_very.f1 -0.0252  -0.114   0.0636 
##  7 pidRepublican             0.0240  -0.0680  0.116  
##  8 polint.f2                 0.169   -0.0809  0.418  
##  9 polint.f3                 0.251    0.0149  0.486  
## 10 polint.f4                 0.337    0.103   0.570</code></pre>
<pre class="r"><code>dabo_gg_LeanR %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower     upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;     &lt;dbl&gt;
##  1 birthyr                  -0.00215 -0.00491  0.000612
##  2 educ.int.f2               0.0810  -0.0487   0.211   
##  3 educ.int.f3               0.0650  -0.0953   0.225   
##  4 educ.int.f4              -0.0405  -0.167    0.0859  
##  5 educ.int.f5               0.00728 -0.149    0.163   
##  6 perceive_extreme_very.f1  0.0500  -0.0423   0.142   
##  7 pidRepublican            -0.182   -0.274   -0.0901  
##  8 polint.f2                 0.0667  -0.196    0.330   
##  9 polint.f3                 0.146   -0.0863   0.379   
## 10 polint.f4                 0.231    0.00306  0.459</code></pre>
<pre class="r"><code>dabo_gg_SolidR %&gt;% select(factor, AME, lower, upper)</code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                         AME    lower    upper
##    &lt;chr&gt;                        &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                  -0.000515 -0.00374  0.00271
##  2 educ.int.f2               0.0310   -0.106    0.168  
##  3 educ.int.f3              -0.0808   -0.256    0.0948 
##  4 educ.int.f4              -0.0602   -0.211    0.0906 
##  5 educ.int.f5              -0.0961   -0.265    0.0723 
##  6 perceive_extreme_very.f1  0.0535   -0.0506   0.158  
##  7 pidRepublican            -0.197    -0.303   -0.0901 
##  8 polint.f2                 0.277     0.0878   0.466  
##  9 polint.f3                 0.356     0.193    0.519  
## 10 polint.f4                 0.501     0.354    0.647</code></pre>
<pre class="r"><code>## Subset further to plot only certain factors ##

dabo_gg_SolidD_2 &lt;- subset(dabo_gg_SolidD, 
                           factor==&quot;pidRepublican&quot;)
dabo_gg_SolidD_2$County = &quot;Solid Democrat&quot;
dabo_gg_SolidD_2$factor &lt;- as.factor(dabo_gg_SolidD_2$factor)
dabo_gg_SolidD_2$var1 &lt;- &quot;Republican&quot;

dabo_gg_LeanD_2 &lt;- subset(dabo_gg_LeanD, 
                          factor==&quot;pidRepublican&quot;)
dabo_gg_LeanD_2$County = &quot;Lean Democrat&quot;
dabo_gg_LeanD_2$factor &lt;- as.factor(dabo_gg_LeanD_2$factor)
dabo_gg_LeanD_2$var1 &lt;- &quot;Republican&quot;

dabo_gg_LeanR_2 &lt;- subset(dabo_gg_LeanR, 
                          factor==&quot;pidRepublican&quot;)
dabo_gg_LeanR_2$County = &quot;Lean Republican&quot;
dabo_gg_LeanR_2$factor &lt;- as.factor(dabo_gg_LeanR_2$factor)
dabo_gg_LeanR_2$var1 &lt;- &quot;Republican&quot;

dabo_gg_SolidR_2 &lt;- subset(dabo_gg_SolidR, 
                           factor==&quot;pidRepublican&quot;)
dabo_gg_SolidR_2$County = &quot;Solid Republican&quot;
dabo_gg_SolidR_2$factor &lt;- as.factor(dabo_gg_SolidR_2$factor)
dabo_gg_SolidR_2$var1 &lt;- &quot;Republican&quot;

bo_gg_da_Combine &lt;- rbind(dabo_gg_SolidD_2, dabo_gg_LeanD_2, dabo_gg_LeanR_2, dabo_gg_SolidR_2)
bo_gg_da_Combine$Office &lt;- &quot;District Attorney&quot;

############## ########### ###########

Finter.house.SolidD &lt;- glm(RCO327_np ~ 
                             pid +
                             perceive_extreme_very.f +
                             polint.f +
                             educ.int.f +
                             birthyr,
                           family=&quot;binomial&quot;,
                           data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Finter.house.LeanD &lt;- glm(RCO327_np ~ 
                            pid +
                            perceive_extreme_very.f +
                            polint.f +
                            educ.int.f +
                            birthyr,
                          family=&quot;binomial&quot;,
                          data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Finter.house.LeanR &lt;- glm(RCO327_np ~ 
                            pid +
                            perceive_extreme_very.f +
                            polint.f +
                            educ.int.f +
                            birthyr,
                          family=&quot;binomial&quot;,
                          data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Finter.house.SolidR &lt;- glm(RCO327_np ~ 
                             pid +
                             perceive_extreme_very.f +
                             polint.f +
                             educ.int.f +
                             birthyr,
                           family=&quot;binomial&quot;,
                           data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

# Using margins() we calculate the marginal effects for each variable:

houseInter.mSolidD &lt;- margins(Finter.house.SolidD)
houseInter.mLeanD &lt;- margins(Finter.house.LeanD)
houseInter.mLeanR &lt;- margins(Finter.house.LeanR)
houseInter.mSolidR &lt;- margins(Finter.house.SolidR)

#we can take results from margins() and plot them ourselves. 
#To clean up the summary a little a little, we convert it to a tibble, 
#then use prefix_strip() and prefix_replace() to tidy the labels. 

housebo_gg_SolidD &lt;- as_tibble(summary(houseInter.mSolidD))
housebo_gg_LeanD &lt;- as_tibble(summary(houseInter.mLeanD))
housebo_gg_LeanR &lt;- as_tibble(summary(houseInter.mLeanR))
housebo_gg_SolidR &lt;- as_tibble(summary(houseInter.mSolidR))

housebo_gg_SolidD %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower   upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;
##  1 birthyr                  -0.00130 -0.00454 0.00195
##  2 educ.int.f2               0.0435  -0.119   0.206  
##  3 educ.int.f3               0.113   -0.0762  0.302  
##  4 educ.int.f4               0.123   -0.0271  0.274  
##  5 educ.int.f5               0.112   -0.0385  0.263  
##  6 perceive_extreme_very.f1 -0.0467  -0.156   0.0627 
##  7 pidRepublican            -0.0964  -0.214   0.0207 
##  8 polint.f2                 0.0355  -0.232   0.303  
##  9 polint.f3                 0.118   -0.116   0.352  
## 10 polint.f4                -0.0233  -0.253   0.207</code></pre>
<pre class="r"><code>housebo_gg_LeanD %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                        AME    lower    upper
##    &lt;chr&gt;                       &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                  -0.00244 -0.00503 0.000151
##  2 educ.int.f2              -0.0410  -0.164   0.0823  
##  3 educ.int.f3               0.106   -0.0644  0.277   
##  4 educ.int.f4               0.0262  -0.0956  0.148   
##  5 educ.int.f5              -0.0811  -0.220   0.0576  
##  6 perceive_extreme_very.f1 -0.0381  -0.126   0.0502  
##  7 pidRepublican             0.0523  -0.0397  0.144   
##  8 polint.f2                -0.0184  -0.294   0.257   
##  9 polint.f3                 0.0167  -0.246   0.280   
## 10 polint.f4                -0.0146  -0.278   0.248</code></pre>
<pre class="r"><code>housebo_gg_LeanR %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                         AME    lower    upper
##    &lt;chr&gt;                        &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                  -0.000522 -0.00318  0.00214
##  2 educ.int.f2               0.143     0.0129   0.273  
##  3 educ.int.f3               0.0739   -0.0850   0.233  
##  4 educ.int.f4              -0.0105   -0.133    0.112  
##  5 educ.int.f5              -0.161    -0.291   -0.0305 
##  6 perceive_extreme_very.f1 -0.0397   -0.128    0.0488 
##  7 pidRepublican            -0.131    -0.219   -0.0420 
##  8 polint.f2                -0.0215   -0.256    0.213  
##  9 polint.f3                 0.0864   -0.128    0.301  
## 10 polint.f4                 0.114    -0.0959   0.323</code></pre>
<pre class="r"><code>housebo_gg_SolidR %&gt;% select(factor, AME, lower, upper)</code></pre>
<pre><code>## # A tibble: 10 x 4
##    factor                         AME    lower    upper
##    &lt;chr&gt;                        &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                   0.000175 -0.00299  0.00334
##  2 educ.int.f2              -0.148    -0.286   -0.0102 
##  3 educ.int.f3              -0.196    -0.367   -0.0258 
##  4 educ.int.f4              -0.132    -0.281    0.0180 
##  5 educ.int.f5              -0.144    -0.313    0.0236 
##  6 perceive_extreme_very.f1  0.0979   -0.00490  0.201  
##  7 pidRepublican            -0.176    -0.284   -0.0678 
##  8 polint.f2                 0.293     0.0867   0.499  
##  9 polint.f3                 0.336     0.155    0.516  
## 10 polint.f4                 0.234     0.0675   0.400</code></pre>
<pre class="r"><code>## Subset further to plot only certain factors ##

housebo_gg_SolidD_2 &lt;- subset(housebo_gg_SolidD, 
                              factor==&quot;pidRepublican&quot;)
housebo_gg_SolidD_2$County = &quot;Solid Democrat&quot;
housebo_gg_SolidD_2$factor &lt;- as.factor(housebo_gg_SolidD_2$factor)
housebo_gg_SolidD_2$var1 &lt;- &quot;Republican&quot;

housebo_gg_LeanD_2 &lt;- subset(housebo_gg_LeanD, 
                             factor==&quot;pidRepublican&quot;)
housebo_gg_LeanD_2$County = &quot;Lean Democrat&quot;
housebo_gg_LeanD_2$factor &lt;- as.factor(housebo_gg_LeanD_2$factor)
housebo_gg_LeanD_2$var1 &lt;- &quot;Republican&quot;

housebo_gg_LeanR_2 &lt;- subset(housebo_gg_LeanR, 
                             factor==&quot;pidRepublican&quot;)
housebo_gg_LeanR_2$County = &quot;Lean Republican&quot;
housebo_gg_LeanR_2$factor &lt;- as.factor(housebo_gg_LeanR_2$factor)
housebo_gg_LeanR_2$var1 &lt;- &quot;Republican&quot;

housebo_gg_SolidR_2 &lt;- subset(housebo_gg_SolidR, 
                              factor==&quot;pidRepublican&quot;)
housebo_gg_SolidR_2$County = &quot;Solid Republican&quot;
housebo_gg_SolidR_2$factor &lt;- as.factor(housebo_gg_SolidR_2$factor)
housebo_gg_SolidR_2$var1 &lt;- &quot;Republican&quot;

bo_gg_house_Combine &lt;- rbind(housebo_gg_SolidD_2, housebo_gg_LeanD_2, housebo_gg_LeanR_2, housebo_gg_SolidR_2)
bo_gg_house_Combine$Office &lt;- &quot;U.S. House&quot;

############## ########### ###########
### Combine Plots ###
pd &lt;- position_dodge(0.5)
bo_gg_Inter_Combine &lt;- rbind(bo_gg_SB_Combine, bo_gg_LEO_Combine, 
                             bo_gg_sher_Combine, bo_gg_da_Combine,
                             bo_gg_house_Combine)

ord &lt;- c(&quot;Solid Democrat&quot;,
         &quot;Lean Democrat&quot;,
         &quot;Lean Republican&quot;,
         &quot;Solid Republican&quot;)

# Correct Order for Plotting
bo_gg_Inter_Combine$County &lt;- factor(bo_gg_Inter_Combine$County,levels=ord)

Inter_Marg_plot &lt;- ggplot(data = bo_gg_Inter_Combine, aes(x = County,
                                                          y = AME, group=Office , ymin = lower, ymax = upper))

Inter_Marg_plot + geom_hline(yintercept = 0, color = &quot;gray80&quot;) +
  geom_pointrange(aes(colour = Office), position=pd) +
  ylim(-0.35,0.35)+
  labs(x = &quot;\n2016 County Presidential Vote&quot;, y = &quot;Average Marginal Effect&quot;, col=&quot;Office&quot;) +
  ggtitle(&quot;&quot;) </code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>##################################################################

### Figure 5. Intra-party differences in predicted probability of choosing nonpartisan elections. ###

##################################################################

# Democrats #
# Full Model (SB)
glm.sb2.D &lt;- glm(RCO322_np ~ 
                   perceive_extreme_very.f +
                   polint.f +
                   educ.int.f +
                   birthyr +
                   Pres_Vote.f,
                 family=&quot;binomial&quot;,
                 data = subset(sbMarginsNP2, pid==&quot;Democrat&quot;))

# Full Model (LEO)
glm.leo2.D &lt;- glm(RCO321_np ~ 
                    perceive_extreme_very.f +
                    polint.f +
                    educ.int.f +
                    birthyr +
                    Pres_Vote.f,
                  family=&quot;binomial&quot;,
                  data = subset(leoMarginsNP2, pid==&quot;Democrat&quot;))

# Full Model (SHER)
glm.sher2.D &lt;- glm(RCO323_np ~ 
                     perceive_extreme_very.f +
                     polint.f +
                     educ.int.f +
                     birthyr +
                     Pres_Vote.f,
                   family=&quot;binomial&quot;,
                   data = subset(sherMarginsNP2, pid==&quot;Democrat&quot;))

# Full Model (DA)
glm.da2.D &lt;- glm(RCO324_np ~ 
                   perceive_extreme_very.f +
                   polint.f +
                   educ.int.f +
                   birthyr +
                   Pres_Vote.f,
                 family=&quot;binomial&quot;,
                 data = subset(daMarginsNP2, pid==&quot;Democrat&quot;))

# Full Model (HOUSE)
glm.house2.D &lt;- glm(RCO327_np ~ 
                      perceive_extreme_very.f +
                      polint.f +
                      educ.int.f +
                      birthyr +
                      Pres_Vote.f,
                    family=&quot;binomial&quot;,
                    data = subset(houseMarginsNP2, pid==&quot;Democrat&quot;))

### Plotting ###

# Using margins() we calculate the marginal effects for each variable:

sb2.1_mD &lt;- margins(glm.sb2.D)
leo2.1_mD &lt;- margins(glm.leo2.D)
sher2.1_mD &lt;- margins(glm.sher2.D)
da2.1_mD &lt;- margins(glm.da2.D)
house2.1_mD &lt;- margins(glm.house2.D)


#we can take results from margins() and plot them ourselves. 
#To clean up the summary a little a little, we convert it to a tibble, 
#then use prefix_strip() and prefix_replace() to tidy the labels. 

bo_gg_D_sb &lt;- as_tibble(summary(sb2.1_mD))
bo_gg_D_leo &lt;- as_tibble(summary(leo2.1_mD))
bo_gg_D_sher &lt;- as_tibble(summary(sher2.1_mD))
bo_gg_D_da &lt;- as_tibble(summary(da2.1_mD))
bo_gg_D_house &lt;- as_tibble(summary(house2.1_mD))

bo_gg_D_sb %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 12 x 4
##    factor                           AME    lower     upper
##    &lt;chr&gt;                          &lt;dbl&gt;    &lt;dbl&gt;     &lt;dbl&gt;
##  1 birthyr                     -0.00252 -0.00440 -0.000646
##  2 educ.int.f2                  0.0631  -0.0302   0.156   
##  3 educ.int.f3                  0.0568  -0.0604   0.174   
##  4 educ.int.f4                  0.0870  -0.00215  0.176   
##  5 educ.int.f5                  0.196    0.101    0.291   
##  6 perceive_extreme_very.f1     0.0703   0.00809  0.132   
##  7 polint.f2                    0.0738  -0.0929   0.241   
##  8 polint.f3                    0.168    0.0150   0.320   
##  9 polint.f4                    0.318    0.168    0.468   
## 10 Pres_Vote.fLean Democrat     0.0246  -0.0532   0.102   
## 11 Pres_Vote.fLean Republican   0.0409  -0.0436   0.125   
## 12 Pres_Vote.fSolid Republican  0.107    0.00815  0.207</code></pre>
<pre class="r"><code>bo_gg_D_leo %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 12 x 4
##    factor                            AME    lower   upper
##    &lt;chr&gt;                           &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;
##  1 birthyr                     -0.000681 -0.00270 0.00134
##  2 educ.int.f2                  0.0194   -0.0775  0.116  
##  3 educ.int.f3                 -0.0454   -0.166   0.0752 
##  4 educ.int.f4                 -0.0447   -0.137   0.0477 
##  5 educ.int.f5                  0.0541   -0.0467  0.155  
##  6 perceive_extreme_very.f1     0.0371   -0.0290  0.103  
##  7 polint.f2                    0.190     0.0343  0.346  
##  8 polint.f3                    0.203     0.0629  0.342  
##  9 polint.f4                    0.272     0.135   0.410  
## 10 Pres_Vote.fLean Democrat    -0.0224   -0.105   0.0597 
## 11 Pres_Vote.fLean Republican   0.0491   -0.0404  0.139  
## 12 Pres_Vote.fSolid Republican  0.0910   -0.0171  0.199</code></pre>
<pre class="r"><code>bo_gg_D_sher %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 12 x 4
##    factor                           AME    lower    upper
##    &lt;chr&gt;                          &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                     -0.00116 -0.00315 0.000824
##  2 educ.int.f2                 -0.0716  -0.167   0.0239  
##  3 educ.int.f3                 -0.00990 -0.129   0.110   
##  4 educ.int.f4                 -0.0109  -0.102   0.0800  
##  5 educ.int.f5                  0.0301  -0.0694  0.130   
##  6 perceive_extreme_very.f1     0.0731   0.00764 0.139   
##  7 polint.f2                    0.0980  -0.0637  0.260   
##  8 polint.f3                    0.152    0.00508 0.298   
##  9 polint.f4                    0.249    0.105   0.394   
## 10 Pres_Vote.fLean Democrat     0.0782  -0.00329 0.160   
## 11 Pres_Vote.fLean Republican   0.111    0.0222  0.199   
## 12 Pres_Vote.fSolid Republican  0.194    0.0893  0.299</code></pre>
<pre class="r"><code>bo_gg_D_da %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 12 x 4
##    factor                           AME    lower    upper
##    &lt;chr&gt;                          &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                     -0.00299 -0.00493 -0.00105
##  2 educ.int.f2                 -0.0238  -0.120    0.0720 
##  3 educ.int.f3                  0.0345  -0.0838   0.153  
##  4 educ.int.f4                 -0.0277  -0.119    0.0633 
##  5 educ.int.f5                  0.0856  -0.0134   0.185  
##  6 perceive_extreme_very.f1     0.0572  -0.00777  0.122  
##  7 polint.f2                    0.155    0.00182  0.309  
##  8 polint.f3                    0.241    0.103    0.378  
##  9 polint.f4                    0.333    0.198    0.468  
## 10 Pres_Vote.fLean Democrat    -0.0198  -0.100    0.0605 
## 11 Pres_Vote.fLean Republican   0.0263  -0.0610   0.114  
## 12 Pres_Vote.fSolid Republican  0.0861  -0.0202   0.192</code></pre>
<pre class="r"><code>bo_gg_D_house %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 12 x 4
##    factor                            AME    lower   upper
##    &lt;chr&gt;                           &lt;dbl&gt;    &lt;dbl&gt;   &lt;dbl&gt;
##  1 birthyr                     -0.000422 -0.00245 0.00160
##  2 educ.int.f2                  0.0116   -0.0852  0.108  
##  3 educ.int.f3                  0.0523   -0.0695  0.174  
##  4 educ.int.f4                  0.0237   -0.0688  0.116  
##  5 educ.int.f5                 -0.00355  -0.104   0.0966 
##  6 perceive_extreme_very.f1    -0.0105   -0.0767  0.0557 
##  7 polint.f2                    0.123    -0.0336  0.279  
##  8 polint.f3                    0.127    -0.0135  0.268  
##  9 polint.f4                    0.129    -0.0100  0.267  
## 10 Pres_Vote.fLean Democrat    -0.0250   -0.107   0.0572 
## 11 Pres_Vote.fLean Republican  -0.00697  -0.0968  0.0829 
## 12 Pres_Vote.fSolid Republican  0.0851   -0.0255  0.196</code></pre>
<pre class="r"><code>ord &lt;- c(&quot;birthyr&quot;,&quot;perceive_extreme_very.f1&quot;,
         &quot;educ.int.f2&quot;,&quot;educ.int.f3&quot;,&quot;educ.int.f4&quot;,&quot;educ.int.f5&quot;,
         &quot;polint.f2&quot;,&quot;polint.f3&quot;,&quot;polint.f4&quot;,
         &quot;Pres_Vote.fLean Democrat&quot;,
         &quot;Pres_Vote.fLean Republican&quot;,
         &quot;Pres_Vote.fSolid Republican&quot;)

bo_gg_D_sb$var1 &lt;- as.character(bo_gg_D_sb$factor)
bo_gg_D_sb$var1 &lt;- factor(bo_gg_D_sb$var1,levels=ord)

bo_gg_D_leo$var1 &lt;- as.character(bo_gg_D_leo$factor)
bo_gg_D_leo$var1 &lt;- factor(bo_gg_D_leo$var1,levels=ord)

bo_gg_D_sher$var1 &lt;- as.character(bo_gg_D_sher$factor)
bo_gg_D_sher$var1 &lt;- factor(bo_gg_D_sher$var1,levels=ord)

bo_gg_D_da$var1 &lt;- as.character(bo_gg_D_da$factor)
bo_gg_D_da$var1 &lt;- factor(bo_gg_D_da$var1,levels=ord)

bo_gg_D_house$var1 &lt;- as.character(bo_gg_D_house$factor)
bo_gg_D_house$var1 &lt;- factor(bo_gg_D_house$var1,levels=ord)

# First make a copy
bo_gg_D_sb_2 &lt;- bo_gg_D_sb
bo_gg_D_leo_2 &lt;- bo_gg_D_leo
bo_gg_D_sher_2 &lt;- bo_gg_D_sher
bo_gg_D_da_2 &lt;- bo_gg_D_da
bo_gg_D_house_2 &lt;- bo_gg_D_house

# Copy and paste this complicated code one-by-one
levels(bo_gg_D_sb_2$var1)[levels(bo_gg_D_sb_2$var1)==&quot;birthyr&quot;] &lt;- &quot;Birth Year&quot;
levels(bo_gg_D_sb_2$var1)[levels(bo_gg_D_sb_2$var1)==&quot;perceive_extreme_very.f1&quot;] &lt;- &quot;Outparty is extreme&quot;
levels(bo_gg_D_sb_2$var1)[levels(bo_gg_D_sb_2$var1)==&quot;educ.int.f2&quot;] &lt;- &quot;Some College&quot;
levels(bo_gg_D_sb_2$var1)[levels(bo_gg_D_sb_2$var1)==&quot;educ.int.f3&quot;] &lt;- &quot;2-Year Degree&quot;
levels(bo_gg_D_sb_2$var1)[levels(bo_gg_D_sb_2$var1)==&quot;educ.int.f4&quot;] &lt;- &quot;4-Year Degree&quot; 
levels(bo_gg_D_sb_2$var1)[levels(bo_gg_D_sb_2$var1)==&quot;educ.int.f5&quot;] &lt;- &quot;Post-Graduate&quot;        
levels(bo_gg_D_sb_2$var1)[levels(bo_gg_D_sb_2$var1)==&quot;polint.f2&quot;] &lt;- &quot;News: Now and then&quot;     
levels(bo_gg_D_sb_2$var1)[levels(bo_gg_D_sb_2$var1)==&quot;polint.f3&quot;] &lt;- &quot;News: Some of the time&quot;     
levels(bo_gg_D_sb_2$var1)[levels(bo_gg_D_sb_2$var1)==&quot;polint.f4&quot;] &lt;- &quot;News: Most of the time&quot;     
levels(bo_gg_D_sb_2$var1)[levels(bo_gg_D_sb_2$var1)==&quot;Pres_Vote.fLean Democrat&quot;] &lt;- &quot;County: Lean Democrat&quot;     
levels(bo_gg_D_sb_2$var1)[levels(bo_gg_D_sb_2$var1)==&quot;Pres_Vote.fLean Republican&quot;] &lt;- &quot;County: Lean Republican&quot;     
levels(bo_gg_D_sb_2$var1)[levels(bo_gg_D_sb_2$var1)==&quot;Pres_Vote.fSolid Republican&quot;] &lt;- &quot;County: Solid Republican&quot;     

# Copy and paste this complicated code one-by-one
levels(bo_gg_D_leo_2$var1)[levels(bo_gg_D_leo_2$var1)==&quot;birthyr&quot;] &lt;- &quot;Birth Year&quot;
levels(bo_gg_D_leo_2$var1)[levels(bo_gg_D_leo_2$var1)==&quot;perceive_extreme_very.f1&quot;] &lt;- &quot;Outparty is extreme&quot;
levels(bo_gg_D_leo_2$var1)[levels(bo_gg_D_leo_2$var1)==&quot;educ.int.f2&quot;] &lt;- &quot;Some College&quot;
levels(bo_gg_D_leo_2$var1)[levels(bo_gg_D_leo_2$var1)==&quot;educ.int.f3&quot;] &lt;- &quot;2-Year Degree&quot;
levels(bo_gg_D_leo_2$var1)[levels(bo_gg_D_leo_2$var1)==&quot;educ.int.f4&quot;] &lt;- &quot;4-Year Degree&quot; 
levels(bo_gg_D_leo_2$var1)[levels(bo_gg_D_leo_2$var1)==&quot;educ.int.f5&quot;] &lt;- &quot;Post-Graduate&quot;        
levels(bo_gg_D_leo_2$var1)[levels(bo_gg_D_leo_2$var1)==&quot;polint.f2&quot;] &lt;- &quot;News: Now and then&quot;     
levels(bo_gg_D_leo_2$var1)[levels(bo_gg_D_leo_2$var1)==&quot;polint.f3&quot;] &lt;- &quot;News: Some of the time&quot;     
levels(bo_gg_D_leo_2$var1)[levels(bo_gg_D_leo_2$var1)==&quot;polint.f4&quot;] &lt;- &quot;News: Most of the time&quot;     
levels(bo_gg_D_leo_2$var1)[levels(bo_gg_D_leo_2$var1)==&quot;Pres_Vote.fLean Democrat&quot;] &lt;- &quot;County: Lean Democrat&quot;     
levels(bo_gg_D_leo_2$var1)[levels(bo_gg_D_leo_2$var1)==&quot;Pres_Vote.fLean Republican&quot;] &lt;- &quot;County: Lean Republican&quot;     
levels(bo_gg_D_leo_2$var1)[levels(bo_gg_D_leo_2$var1)==&quot;Pres_Vote.fSolid Republican&quot;] &lt;- &quot;County: Solid Republican&quot;     

# Copy and paste this complicated code one-by-one
levels(bo_gg_D_sher_2$var1)[levels(bo_gg_D_sher_2$var1)==&quot;birthyr&quot;] &lt;- &quot;Birth Year&quot;
levels(bo_gg_D_sher_2$var1)[levels(bo_gg_D_sher_2$var1)==&quot;perceive_extreme_very.f1&quot;] &lt;- &quot;Outparty is extreme&quot;
levels(bo_gg_D_sher_2$var1)[levels(bo_gg_D_sher_2$var1)==&quot;educ.int.f2&quot;] &lt;- &quot;Some College&quot;
levels(bo_gg_D_sher_2$var1)[levels(bo_gg_D_sher_2$var1)==&quot;educ.int.f3&quot;] &lt;- &quot;2-Year Degree&quot;
levels(bo_gg_D_sher_2$var1)[levels(bo_gg_D_sher_2$var1)==&quot;educ.int.f4&quot;] &lt;- &quot;4-Year Degree&quot; 
levels(bo_gg_D_sher_2$var1)[levels(bo_gg_D_sher_2$var1)==&quot;educ.int.f5&quot;] &lt;- &quot;Post-Graduate&quot;       
levels(bo_gg_D_sher_2$var1)[levels(bo_gg_D_sher_2$var1)==&quot;polint.f2&quot;] &lt;- &quot;News: Now and then&quot;     
levels(bo_gg_D_sher_2$var1)[levels(bo_gg_D_sher_2$var1)==&quot;polint.f3&quot;] &lt;- &quot;News: Some of the time&quot;     
levels(bo_gg_D_sher_2$var1)[levels(bo_gg_D_sher_2$var1)==&quot;polint.f4&quot;] &lt;- &quot;News: Most of the time&quot;     
levels(bo_gg_D_sher_2$var1)[levels(bo_gg_D_sher_2$var1)==&quot;Pres_Vote.fLean Democrat&quot;] &lt;- &quot;County: Lean Democrat&quot;     
levels(bo_gg_D_sher_2$var1)[levels(bo_gg_D_sher_2$var1)==&quot;Pres_Vote.fLean Republican&quot;] &lt;- &quot;County: Lean Republican&quot;     
levels(bo_gg_D_sher_2$var1)[levels(bo_gg_D_sher_2$var1)==&quot;Pres_Vote.fSolid Republican&quot;] &lt;- &quot;County: Solid Republican&quot;     

# Copy and paste this complicated code one-by-one
levels(bo_gg_D_da_2$var1)[levels(bo_gg_D_da_2$var1)==&quot;birthyr&quot;] &lt;- &quot;Birth Year&quot;
levels(bo_gg_D_da_2$var1)[levels(bo_gg_D_da_2$var1)==&quot;perceive_extreme_very.f1&quot;] &lt;- &quot;Outparty is extreme&quot;
levels(bo_gg_D_da_2$var1)[levels(bo_gg_D_da_2$var1)==&quot;educ.int.f2&quot;] &lt;- &quot;Some College&quot;
levels(bo_gg_D_da_2$var1)[levels(bo_gg_D_da_2$var1)==&quot;educ.int.f3&quot;] &lt;- &quot;2-Year Degree&quot;
levels(bo_gg_D_da_2$var1)[levels(bo_gg_D_da_2$var1)==&quot;educ.int.f4&quot;] &lt;- &quot;4-Year Degree&quot; 
levels(bo_gg_D_da_2$var1)[levels(bo_gg_D_da_2$var1)==&quot;educ.int.f5&quot;] &lt;- &quot;Post-Graduate&quot;        
levels(bo_gg_D_da_2$var1)[levels(bo_gg_D_da_2$var1)==&quot;polint.f2&quot;] &lt;- &quot;News: Now and then&quot;     
levels(bo_gg_D_da_2$var1)[levels(bo_gg_D_da_2$var1)==&quot;polint.f3&quot;] &lt;- &quot;News: Some of the time&quot;     
levels(bo_gg_D_da_2$var1)[levels(bo_gg_D_da_2$var1)==&quot;polint.f4&quot;] &lt;- &quot;News: Most of the time&quot;     
levels(bo_gg_D_da_2$var1)[levels(bo_gg_D_da_2$var1)==&quot;Pres_Vote.fLean Democrat&quot;] &lt;- &quot;County: Lean Democrat&quot;     
levels(bo_gg_D_da_2$var1)[levels(bo_gg_D_da_2$var1)==&quot;Pres_Vote.fLean Republican&quot;] &lt;- &quot;County: Lean Republican&quot;     
levels(bo_gg_D_da_2$var1)[levels(bo_gg_D_da_2$var1)==&quot;Pres_Vote.fSolid Republican&quot;] &lt;- &quot;County: Solid Republican&quot;     

# Copy and paste this complicated code one-by-one
levels(bo_gg_D_house_2$var1)[levels(bo_gg_D_house_2$var1)==&quot;birthyr&quot;] &lt;- &quot;Birth Year&quot;
levels(bo_gg_D_house_2$var1)[levels(bo_gg_D_house_2$var1)==&quot;perceive_extreme_very.f1&quot;] &lt;- &quot;Outparty is extreme&quot;
levels(bo_gg_D_house_2$var1)[levels(bo_gg_D_house_2$var1)==&quot;educ.int.f2&quot;] &lt;- &quot;Some College&quot;
levels(bo_gg_D_house_2$var1)[levels(bo_gg_D_house_2$var1)==&quot;educ.int.f3&quot;] &lt;- &quot;2-Year Degree&quot;
levels(bo_gg_D_house_2$var1)[levels(bo_gg_D_house_2$var1)==&quot;educ.int.f4&quot;] &lt;- &quot;4-Year Degree&quot; 
levels(bo_gg_D_house_2$var1)[levels(bo_gg_D_house_2$var1)==&quot;educ.int.f5&quot;] &lt;- &quot;Post-Graduate&quot;        
levels(bo_gg_D_house_2$var1)[levels(bo_gg_D_house_2$var1)==&quot;polint.f2&quot;] &lt;- &quot;News: Now and then&quot;     
levels(bo_gg_D_house_2$var1)[levels(bo_gg_D_house_2$var1)==&quot;polint.f3&quot;] &lt;- &quot;News: Some of the time&quot;     
levels(bo_gg_D_house_2$var1)[levels(bo_gg_D_house_2$var1)==&quot;polint.f4&quot;] &lt;- &quot;News: Most of the time&quot;     
levels(bo_gg_D_house_2$var1)[levels(bo_gg_D_house_2$var1)==&quot;Pres_Vote.fLean Democrat&quot;] &lt;- &quot;County: Lean Democrat&quot;     
levels(bo_gg_D_house_2$var1)[levels(bo_gg_D_house_2$var1)==&quot;Pres_Vote.fLean Republican&quot;] &lt;- &quot;County: Lean Republican&quot;     
levels(bo_gg_D_house_2$var1)[levels(bo_gg_D_house_2$var1)==&quot;Pres_Vote.fSolid Republican&quot;] &lt;- &quot;County: Solid Republican&quot;     

### Combine Plots ###
pd &lt;- position_dodge(0.3)

## Subset further to plot only certain factors ##

bo_gg_D_sb_3 &lt;- subset(bo_gg_D_sb_2, 
                       factor==&quot;perceive_extreme_very.f1&quot; |
                         factor==&quot;educ.int.f5&quot; |
                         factor==&quot;polint.f4&quot; |
                         factor==&quot;Pres_Vote.fSolid Republican&quot;)
bo_gg_D_sb_3$pid = &quot;Democrat&quot;
bo_gg_D_sb_3$Office = &quot;School Board&quot;

bo_gg_D_leo_3 &lt;- subset(bo_gg_D_leo_2, 
                        factor==&quot;perceive_extreme_very.f1&quot; |
                          factor==&quot;educ.int.f5&quot; |
                          factor==&quot;polint.f4&quot; |
                          factor==&quot;Pres_Vote.fSolid Republican&quot;)
bo_gg_D_leo_3$pid = &quot;Democrat&quot;
bo_gg_D_leo_3$Office = &quot;Local Election Official&quot;


bo_gg_D_sher_3 &lt;- subset(bo_gg_D_sher_2, 
                         factor==&quot;perceive_extreme_very.f1&quot; |
                           factor==&quot;educ.int.f5&quot; |
                           factor==&quot;polint.f4&quot; |
                           factor==&quot;Pres_Vote.fSolid Republican&quot;)
bo_gg_D_sher_3$pid = &quot;Democrat&quot;
bo_gg_D_sher_3$Office = &quot;Sheriff&quot;

bo_gg_D_da_3 &lt;- subset(bo_gg_D_da_2, 
                       factor==&quot;perceive_extreme_very.f1&quot; |
                         factor==&quot;educ.int.f5&quot; |
                         factor==&quot;polint.f4&quot; |
                         factor==&quot;Pres_Vote.fSolid Republican&quot;)
bo_gg_D_da_3$pid = &quot;Democrat&quot;
bo_gg_D_da_3$Office = &quot;District Attorney&quot;

bo_gg_D_house_3 &lt;- subset(bo_gg_D_house_2, 
                          factor==&quot;perceive_extreme_very.f1&quot; |
                            factor==&quot;educ.int.f5&quot; |
                            factor==&quot;polint.f4&quot; |
                            factor==&quot;Pres_Vote.fSolid Republican&quot;)
bo_gg_D_house_3$pid = &quot;Democrat&quot;
bo_gg_D_house_3$Office = &quot;U.S. House&quot;


np_Intra_D &lt;- rbind(bo_gg_D_sb_3, bo_gg_D_leo_3, bo_gg_D_sher_3,
                    bo_gg_D_da_3, bo_gg_D_house_3)

#####


np_Intra_D_plot &lt;- ggplot(data = np_Intra_D, aes(x = var1,
                                                 y = AME, group=Office , ymin = lower, ymax = upper))

np_Intra_D_plot</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>np_Intra_D_plot &lt;- np_Intra_D_plot + geom_hline(yintercept = 0, color = &quot;gray80&quot;) +
  geom_pointrange(aes(colour = Office), position = pd) + 
  coord_flip(ylim = c(-0.5, 0.5)) +
  labs(x = NULL, y = &quot;Average Marginal Effect&quot;, col=&quot;Office&quot;) +
  ggtitle(&quot;Among Democrats&quot;) +
  scale_x_discrete(labels=c(&quot;County: Solid Republican&quot; = &quot;County:\nSolid Republican&quot;, 
                            &quot;News: Most of the time&quot; = &quot;News:\nMost of the time&quot;,
                            &quot;Post-Graduate&quot; = &quot;Education:\nPost-Graduate&quot;,
                            &quot;Outparty is extreme&quot; = &quot;Outparty:\nis extreme&quot;))


#### REPUBLICANS ####

# Full Model (SB)
glm.sb2.R &lt;- glm(RCO322_np ~ 
                   perceive_extreme_very.f +
                   polint.f +
                   educ.int.f +
                   birthyr +
                   Pres_Vote.f,
                 family=&quot;binomial&quot;,
                 data = subset(sbMarginsNP2, pid==&quot;Republican&quot;))

# Full Model (LEO)
glm.leo2.R &lt;- glm(RCO321_np ~ 
                    perceive_extreme_very.f +
                    polint.f +
                    educ.int.f +
                    birthyr +
                    Pres_Vote.f,
                  family=&quot;binomial&quot;,
                  data = subset(leoMarginsNP2, pid==&quot;Republican&quot;))

# Full Model (SHER)
glm.sher2.R &lt;- glm(RCO323_np ~ 
                     perceive_extreme_very.f +
                     polint.f +
                     educ.int.f +
                     birthyr +
                     Pres_Vote.f,
                   family=&quot;binomial&quot;,
                   data = subset(sherMarginsNP2, pid==&quot;Republican&quot;))

# Full Model (DA)
glm.da2.R &lt;- glm(RCO324_np ~ 
                   perceive_extreme_very.f +
                   polint.f +
                   educ.int.f +
                   birthyr +
                   Pres_Vote.f,
                 family=&quot;binomial&quot;,
                 data = subset(daMarginsNP2, pid==&quot;Republican&quot;))

# Full Model (HOUSE)
glm.house2.R &lt;- glm(RCO327_np ~ 
                      perceive_extreme_very.f +
                      polint.f +
                      educ.int.f +
                      birthyr +
                      Pres_Vote.f,
                    family=&quot;binomial&quot;,
                    data = subset(houseMarginsNP2, pid==&quot;Republican&quot;))

### Plotting ###

# Using margins() we calculate the marginal effects for each variable:

sb2.1_mR &lt;- margins(glm.sb2.R)
leo2.1_mR &lt;- margins(glm.leo2.R)
sher2.1_mR &lt;- margins(glm.sher2.R)
da2.1_mR &lt;- margins(glm.da2.R)
house2.1_mR &lt;- margins(glm.house2.R)


#we can take results from margins() and plot them ourselves. 
#To clean up the summary a little a little, we convert it to a tibble, 
#then use prefix_strip() and prefix_replace() to tidy the labels. 

bo_gg_R_sb &lt;- as_tibble(summary(sb2.1_mR))
bo_gg_R_leo &lt;- as_tibble(summary(leo2.1_mR))
bo_gg_R_sher &lt;- as_tibble(summary(sher2.1_mR))
bo_gg_R_da &lt;- as_tibble(summary(da2.1_mR))
bo_gg_R_house &lt;- as_tibble(summary(house2.1_mR))

bo_gg_R_sb %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 12 x 4
##    factor                           AME    lower     upper
##    &lt;chr&gt;                          &lt;dbl&gt;    &lt;dbl&gt;     &lt;dbl&gt;
##  1 birthyr                     -0.00268 -0.00483 -0.000529
##  2 educ.int.f2                  0.0582  -0.0420   0.158   
##  3 educ.int.f3                 -0.0231  -0.150    0.104   
##  4 educ.int.f4                  0.0667  -0.0362   0.170   
##  5 educ.int.f5                 -0.0163  -0.141    0.108   
##  6 perceive_extreme_very.f1    -0.0294  -0.104    0.0451  
##  7 polint.f2                   -0.0483  -0.290    0.194   
##  8 polint.f3                    0.0608  -0.165    0.287   
##  9 polint.f4                    0.0908  -0.132    0.313   
## 10 Pres_Vote.fLean Democrat    -0.128   -0.250   -0.00521 
## 11 Pres_Vote.fLean Republican  -0.147   -0.269   -0.0253  
## 12 Pres_Vote.fSolid Republican -0.174   -0.295   -0.0526</code></pre>
<pre class="r"><code>bo_gg_R_leo %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 12 x 4
##    factor                           AME    lower      upper
##    &lt;chr&gt;                          &lt;dbl&gt;    &lt;dbl&gt;      &lt;dbl&gt;
##  1 birthyr                     -0.00216 -0.00429 -0.0000214
##  2 educ.int.f2                  0.0386  -0.0613   0.138    
##  3 educ.int.f3                  0.0578  -0.0689   0.185    
##  4 educ.int.f4                  0.0350  -0.0674   0.137    
##  5 educ.int.f5                 -0.0818  -0.199    0.0350   
##  6 perceive_extreme_very.f1    -0.0125  -0.0859   0.0610   
##  7 polint.f2                    0.223    0.0261   0.420    
##  8 polint.f3                    0.273    0.0948   0.450    
##  9 polint.f4                    0.255    0.0840   0.427    
## 10 Pres_Vote.fLean Democrat     0.0965  -0.0284   0.222    
## 11 Pres_Vote.fLean Republican  -0.0367  -0.160    0.0864   
## 12 Pres_Vote.fSolid Republican -0.0164  -0.139    0.106</code></pre>
<pre class="r"><code>bo_gg_R_sher %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 12 x 4
##    factor                           AME    lower    upper
##    &lt;chr&gt;                          &lt;dbl&gt;    &lt;dbl&gt;    &lt;dbl&gt;
##  1 birthyr                     -0.00383 -0.00595 -0.00171
##  2 educ.int.f2                 -0.0115  -0.112    0.0888 
##  3 educ.int.f3                  0.0618  -0.0655   0.189  
##  4 educ.int.f4                  0.0557  -0.0475   0.159  
##  5 educ.int.f5                  0.0352  -0.0884   0.159  
##  6 perceive_extreme_very.f1    -0.0265  -0.101    0.0480 
##  7 polint.f2                    0.151   -0.0867   0.389  
##  8 polint.f3                    0.148   -0.0733   0.368  
##  9 polint.f4                    0.168   -0.0499   0.385  
## 10 Pres_Vote.fLean Democrat     0.0191  -0.106    0.144  
## 11 Pres_Vote.fLean Republican  -0.0871  -0.211    0.0370 
## 12 Pres_Vote.fSolid Republican -0.0558  -0.179    0.0678</code></pre>
<pre class="r"><code>bo_gg_R_da %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 12 x 4
##    factor                           AME    lower     upper
##    &lt;chr&gt;                          &lt;dbl&gt;    &lt;dbl&gt;     &lt;dbl&gt;
##  1 birthyr                     -0.00174 -0.00391  0.000439
##  2 educ.int.f2                  0.0292  -0.0715   0.130   
##  3 educ.int.f3                  0.0287  -0.0984   0.156   
##  4 educ.int.f4                  0.0378  -0.0656   0.141   
##  5 educ.int.f5                  0.0596  -0.0652   0.184   
##  6 perceive_extreme_very.f1    -0.0233  -0.0980   0.0514  
##  7 polint.f2                    0.0815  -0.152    0.315   
##  8 polint.f3                    0.126   -0.0907   0.342   
##  9 polint.f4                    0.184   -0.0288   0.397   
## 10 Pres_Vote.fLean Democrat     0.00143 -0.125    0.128   
## 11 Pres_Vote.fLean Republican  -0.133   -0.258   -0.00846 
## 12 Pres_Vote.fSolid Republican -0.0821  -0.207    0.0428</code></pre>
<pre class="r"><code>bo_gg_R_house %&gt;% select(factor, AME, lower, upper) </code></pre>
<pre><code>## # A tibble: 12 x 4
##    factor                           AME    lower     upper
##    &lt;chr&gt;                          &lt;dbl&gt;    &lt;dbl&gt;     &lt;dbl&gt;
##  1 birthyr                     -0.00217 -0.00421 -0.000129
##  2 educ.int.f2                 -0.0148  -0.113    0.0830  
##  3 educ.int.f3                 -0.0220  -0.145    0.101   
##  4 educ.int.f4                 -0.0354  -0.135    0.0644  
##  5 educ.int.f5                 -0.207   -0.311   -0.103   
##  6 perceive_extreme_very.f1    -0.0186  -0.0891   0.0520  
##  7 polint.f2                    0.00320 -0.219    0.225   
##  8 polint.f3                    0.139   -0.0703   0.348   
##  9 polint.f4                    0.0210  -0.184    0.226   
## 10 Pres_Vote.fLean Democrat     0.133    0.0107   0.255   
## 11 Pres_Vote.fLean Republican  -0.0524  -0.170    0.0654  
## 12 Pres_Vote.fSolid Republican -0.00733 -0.126    0.111</code></pre>
<pre class="r"><code>ord &lt;- c(&quot;birthyr&quot;,&quot;perceive_extreme_very.f1&quot;,
         &quot;educ.int.f2&quot;,&quot;educ.int.f3&quot;,&quot;educ.int.f4&quot;,&quot;educ.int.f5&quot;,
         &quot;polint.f2&quot;,&quot;polint.f3&quot;,&quot;polint.f4&quot;,
         &quot;Pres_Vote.fLean Democrat&quot;,
         &quot;Pres_Vote.fLean Republican&quot;,
         &quot;Pres_Vote.fSolid Republican&quot;)

bo_gg_R_sb$var1 &lt;- as.character(bo_gg_R_sb$factor)
bo_gg_R_sb$var1 &lt;- factor(bo_gg_R_sb$var1,levels=ord)

bo_gg_R_leo$var1 &lt;- as.character(bo_gg_R_leo$factor)
bo_gg_R_leo$var1 &lt;- factor(bo_gg_R_leo$var1,levels=ord)

bo_gg_R_sher$var1 &lt;- as.character(bo_gg_R_sher$factor)
bo_gg_R_sher$var1 &lt;- factor(bo_gg_R_sher$var1,levels=ord)

bo_gg_R_da$var1 &lt;- as.character(bo_gg_R_da$factor)
bo_gg_R_da$var1 &lt;- factor(bo_gg_R_da$var1,levels=ord)

bo_gg_R_house$var1 &lt;- as.character(bo_gg_R_house$factor)
bo_gg_R_house$var1 &lt;- factor(bo_gg_R_house$var1,levels=ord)

# First make a copy
bo_gg_R_sb_2 &lt;- bo_gg_R_sb
bo_gg_R_leo_2 &lt;- bo_gg_R_leo
bo_gg_R_sher_2 &lt;- bo_gg_R_sher
bo_gg_R_da_2 &lt;- bo_gg_R_da
bo_gg_R_house_2 &lt;- bo_gg_R_house

# Copy and paste this complicated code one-by-one
levels(bo_gg_R_sb_2$var1)[levels(bo_gg_R_sb_2$var1)==&quot;birthyr&quot;] &lt;- &quot;Birth Year&quot;
levels(bo_gg_R_sb_2$var1)[levels(bo_gg_R_sb_2$var1)==&quot;perceive_extreme_very.f1&quot;] &lt;- &quot;Outparty is extreme&quot;
levels(bo_gg_R_sb_2$var1)[levels(bo_gg_R_sb_2$var1)==&quot;educ.int.f2&quot;] &lt;- &quot;Some College&quot;
levels(bo_gg_R_sb_2$var1)[levels(bo_gg_R_sb_2$var1)==&quot;educ.int.f3&quot;] &lt;- &quot;2-Year Degree&quot;
levels(bo_gg_R_sb_2$var1)[levels(bo_gg_R_sb_2$var1)==&quot;educ.int.f4&quot;] &lt;- &quot;4-Year Degree&quot; 
levels(bo_gg_R_sb_2$var1)[levels(bo_gg_R_sb_2$var1)==&quot;educ.int.f5&quot;] &lt;- &quot;Post-Graduate&quot;        
levels(bo_gg_R_sb_2$var1)[levels(bo_gg_R_sb_2$var1)==&quot;polint.f2&quot;] &lt;- &quot;News: Now and then&quot;     
levels(bo_gg_R_sb_2$var1)[levels(bo_gg_R_sb_2$var1)==&quot;polint.f3&quot;] &lt;- &quot;News: Some of the time&quot;     
levels(bo_gg_R_sb_2$var1)[levels(bo_gg_R_sb_2$var1)==&quot;polint.f4&quot;] &lt;- &quot;News: Most of the time&quot;     
levels(bo_gg_R_sb_2$var1)[levels(bo_gg_R_sb_2$var1)==&quot;Pres_Vote.fLean Democrat&quot;] &lt;- &quot;County: Lean Democrat&quot;     
levels(bo_gg_R_sb_2$var1)[levels(bo_gg_R_sb_2$var1)==&quot;Pres_Vote.fLean Republican&quot;] &lt;- &quot;County: Lean Republican&quot;     
levels(bo_gg_R_sb_2$var1)[levels(bo_gg_R_sb_2$var1)==&quot;Pres_Vote.fSolid Republican&quot;] &lt;- &quot;County: Solid Republican&quot;     

# Copy and paste this complicated code one-by-one
levels(bo_gg_R_leo_2$var1)[levels(bo_gg_R_leo_2$var1)==&quot;birthyr&quot;] &lt;- &quot;Birth Year&quot;
levels(bo_gg_R_leo_2$var1)[levels(bo_gg_R_leo_2$var1)==&quot;perceive_extreme_very.f1&quot;] &lt;- &quot;Outparty is extreme&quot;
levels(bo_gg_R_leo_2$var1)[levels(bo_gg_R_leo_2$var1)==&quot;educ.int.f2&quot;] &lt;- &quot;Some College&quot;
levels(bo_gg_R_leo_2$var1)[levels(bo_gg_R_leo_2$var1)==&quot;educ.int.f3&quot;] &lt;- &quot;2-Year Degree&quot;
levels(bo_gg_R_leo_2$var1)[levels(bo_gg_R_leo_2$var1)==&quot;educ.int.f4&quot;] &lt;- &quot;4-Year Degree&quot; 
levels(bo_gg_R_leo_2$var1)[levels(bo_gg_R_leo_2$var1)==&quot;educ.int.f5&quot;] &lt;- &quot;Post-Graduate&quot;        
levels(bo_gg_R_leo_2$var1)[levels(bo_gg_R_leo_2$var1)==&quot;polint.f2&quot;] &lt;- &quot;News: Now and then&quot;     
levels(bo_gg_R_leo_2$var1)[levels(bo_gg_R_leo_2$var1)==&quot;polint.f3&quot;] &lt;- &quot;News: Some of the time&quot;     
levels(bo_gg_R_leo_2$var1)[levels(bo_gg_R_leo_2$var1)==&quot;polint.f4&quot;] &lt;- &quot;News: Most of the time&quot;     
levels(bo_gg_R_leo_2$var1)[levels(bo_gg_R_leo_2$var1)==&quot;Pres_Vote.fLean Democrat&quot;] &lt;- &quot;County: Lean Democrat&quot;     
levels(bo_gg_R_leo_2$var1)[levels(bo_gg_R_leo_2$var1)==&quot;Pres_Vote.fLean Republican&quot;] &lt;- &quot;County: Lean Republican&quot;     
levels(bo_gg_R_leo_2$var1)[levels(bo_gg_R_leo_2$var1)==&quot;Pres_Vote.fSolid Republican&quot;] &lt;- &quot;County: Solid Republican&quot;     

# Copy and paste this complicated code one-by-one
levels(bo_gg_R_sher_2$var1)[levels(bo_gg_R_sher_2$var1)==&quot;birthyr&quot;] &lt;- &quot;Birth Year&quot;
levels(bo_gg_R_sher_2$var1)[levels(bo_gg_R_sher_2$var1)==&quot;perceive_extreme_very.f1&quot;] &lt;- &quot;Outparty is extreme&quot;
levels(bo_gg_R_sher_2$var1)[levels(bo_gg_R_sher_2$var1)==&quot;educ.int.f2&quot;] &lt;- &quot;Some College&quot;
levels(bo_gg_R_sher_2$var1)[levels(bo_gg_R_sher_2$var1)==&quot;educ.int.f3&quot;] &lt;- &quot;2-Year Degree&quot;
levels(bo_gg_R_sher_2$var1)[levels(bo_gg_R_sher_2$var1)==&quot;educ.int.f4&quot;] &lt;- &quot;4-Year Degree&quot; 
levels(bo_gg_R_sher_2$var1)[levels(bo_gg_R_sher_2$var1)==&quot;educ.int.f5&quot;] &lt;- &quot;Post-Graduate&quot;        
levels(bo_gg_R_sher_2$var1)[levels(bo_gg_R_sher_2$var1)==&quot;polint.f2&quot;] &lt;- &quot;News: Now and then&quot;     
levels(bo_gg_R_sher_2$var1)[levels(bo_gg_R_sher_2$var1)==&quot;polint.f3&quot;] &lt;- &quot;News: Some of the time&quot;     
levels(bo_gg_R_sher_2$var1)[levels(bo_gg_R_sher_2$var1)==&quot;polint.f4&quot;] &lt;- &quot;News: Most of the time&quot;     
levels(bo_gg_R_sher_2$var1)[levels(bo_gg_R_sher_2$var1)==&quot;Pres_Vote.fLean Democrat&quot;] &lt;- &quot;County: Lean Democrat&quot;     
levels(bo_gg_R_sher_2$var1)[levels(bo_gg_R_sher_2$var1)==&quot;Pres_Vote.fLean Republican&quot;] &lt;- &quot;County: Lean Republican&quot;     
levels(bo_gg_R_sher_2$var1)[levels(bo_gg_R_sher_2$var1)==&quot;Pres_Vote.fSolid Republican&quot;] &lt;- &quot;County: Solid Republican&quot;     

# Copy and paste this complicated code one-by-one
levels(bo_gg_R_da_2$var1)[levels(bo_gg_R_da_2$var1)==&quot;birthyr&quot;] &lt;- &quot;Birth Year&quot;
levels(bo_gg_R_da_2$var1)[levels(bo_gg_R_da_2$var1)==&quot;perceive_extreme_very.f1&quot;] &lt;- &quot;Outparty is extreme&quot;
levels(bo_gg_R_da_2$var1)[levels(bo_gg_R_da_2$var1)==&quot;educ.int.f2&quot;] &lt;- &quot;Some College&quot;
levels(bo_gg_R_da_2$var1)[levels(bo_gg_R_da_2$var1)==&quot;educ.int.f3&quot;] &lt;- &quot;2-Year Degree&quot;
levels(bo_gg_R_da_2$var1)[levels(bo_gg_R_da_2$var1)==&quot;educ.int.f4&quot;] &lt;- &quot;4-Year Degree&quot; 
levels(bo_gg_R_da_2$var1)[levels(bo_gg_R_da_2$var1)==&quot;educ.int.f5&quot;] &lt;- &quot;Post-Graduate&quot;        
levels(bo_gg_R_da_2$var1)[levels(bo_gg_R_da_2$var1)==&quot;polint.f2&quot;] &lt;- &quot;News: Now and then&quot;     
levels(bo_gg_R_da_2$var1)[levels(bo_gg_R_da_2$var1)==&quot;polint.f3&quot;] &lt;- &quot;News: Some of the time&quot;     
levels(bo_gg_R_da_2$var1)[levels(bo_gg_R_da_2$var1)==&quot;polint.f4&quot;] &lt;- &quot;News: Most of the time&quot;     
levels(bo_gg_R_da_2$var1)[levels(bo_gg_R_da_2$var1)==&quot;Pres_Vote.fLean Democrat&quot;] &lt;- &quot;County: Lean Democrat&quot;     
levels(bo_gg_R_da_2$var1)[levels(bo_gg_R_da_2$var1)==&quot;Pres_Vote.fLean Republican&quot;] &lt;- &quot;County: Lean Republican&quot;     
levels(bo_gg_R_da_2$var1)[levels(bo_gg_R_da_2$var1)==&quot;Pres_Vote.fSolid Republican&quot;] &lt;- &quot;County: Solid Republican&quot;     

# Copy and paste this complicated code one-by-one
levels(bo_gg_R_house_2$var1)[levels(bo_gg_R_house_2$var1)==&quot;birthyr&quot;] &lt;- &quot;Birth Year&quot;
levels(bo_gg_R_house_2$var1)[levels(bo_gg_R_house_2$var1)==&quot;perceive_extreme_very.f1&quot;] &lt;- &quot;Outparty is extreme&quot;
levels(bo_gg_R_house_2$var1)[levels(bo_gg_R_house_2$var1)==&quot;educ.int.f2&quot;] &lt;- &quot;Some College&quot;
levels(bo_gg_R_house_2$var1)[levels(bo_gg_R_house_2$var1)==&quot;educ.int.f3&quot;] &lt;- &quot;2-Year Degree&quot;
levels(bo_gg_R_house_2$var1)[levels(bo_gg_R_house_2$var1)==&quot;educ.int.f4&quot;] &lt;- &quot;4-Year Degree&quot; 
levels(bo_gg_R_house_2$var1)[levels(bo_gg_R_house_2$var1)==&quot;educ.int.f5&quot;] &lt;- &quot;Post-Graduate&quot;        
levels(bo_gg_R_house_2$var1)[levels(bo_gg_R_house_2$var1)==&quot;polint.f2&quot;] &lt;- &quot;News: Now and then&quot;     
levels(bo_gg_R_house_2$var1)[levels(bo_gg_R_house_2$var1)==&quot;polint.f3&quot;] &lt;- &quot;News: Some of the time&quot;     
levels(bo_gg_R_house_2$var1)[levels(bo_gg_R_house_2$var1)==&quot;polint.f4&quot;] &lt;- &quot;News: Most of the time&quot;     
levels(bo_gg_R_house_2$var1)[levels(bo_gg_R_house_2$var1)==&quot;Pres_Vote.fLean Democrat&quot;] &lt;- &quot;County: Lean Democrat&quot;     
levels(bo_gg_R_house_2$var1)[levels(bo_gg_R_house_2$var1)==&quot;Pres_Vote.fLean Republican&quot;] &lt;- &quot;County: Lean Republican&quot;     
levels(bo_gg_R_house_2$var1)[levels(bo_gg_R_house_2$var1)==&quot;Pres_Vote.fSolid Republican&quot;] &lt;- &quot;County: Solid Republican&quot;     


### Combine Plots ###
pd &lt;- position_dodge(0.3)

## Subset further to plot only certain factors ##

bo_gg_R_sb_3 &lt;- subset(bo_gg_R_sb_2, 
                       factor==&quot;perceive_extreme_very.f1&quot; |
                         factor==&quot;educ.int.f5&quot; |
                         factor==&quot;polint.f4&quot; |
                         factor==&quot;Pres_Vote.fSolid Republican&quot;)
bo_gg_R_sb_3$pid = &quot;Republican&quot;
bo_gg_R_sb_3$Office = &quot;School Board&quot;

bo_gg_R_leo_3 &lt;- subset(bo_gg_R_leo_2, 
                        factor==&quot;perceive_extreme_very.f1&quot; |
                          factor==&quot;educ.int.f5&quot; |
                          factor==&quot;polint.f4&quot; |
                          factor==&quot;Pres_Vote.fSolid Republican&quot;)
bo_gg_R_leo_3$pid = &quot;Republican&quot;
bo_gg_R_leo_3$Office = &quot;Local Election Official&quot;


bo_gg_R_sher_3 &lt;- subset(bo_gg_R_sher_2, 
                         factor==&quot;perceive_extreme_very.f1&quot; |
                           factor==&quot;educ.int.f5&quot; |
                           factor==&quot;polint.f4&quot; |
                           factor==&quot;Pres_Vote.fSolid Republican&quot;)
bo_gg_R_sher_3$pid = &quot;Republican&quot;
bo_gg_R_sher_3$Office = &quot;Sheriff&quot;

bo_gg_R_da_3 &lt;- subset(bo_gg_R_da_2, 
                       factor==&quot;perceive_extreme_very.f1&quot; |
                         factor==&quot;educ.int.f5&quot; |
                         factor==&quot;polint.f4&quot; |
                         factor==&quot;Pres_Vote.fSolid Republican&quot;)
bo_gg_R_da_3$pid = &quot;Republican&quot;
bo_gg_R_da_3$Office = &quot;District Attorney&quot;

bo_gg_R_house_3 &lt;- subset(bo_gg_R_house_2, 
                          factor==&quot;perceive_extreme_very.f1&quot; |
                            factor==&quot;educ.int.f5&quot; |
                            factor==&quot;polint.f4&quot; |
                            factor==&quot;Pres_Vote.fSolid Republican&quot;)
bo_gg_R_house_3$pid = &quot;Republican&quot;
bo_gg_R_house_3$Office = &quot;U.S. House&quot;


np_Intra_R &lt;- rbind(bo_gg_R_sb_3, bo_gg_R_leo_3, bo_gg_R_sher_3,
                    bo_gg_R_da_3, bo_gg_R_house_3)

#####


np_Intra_R_plot &lt;- ggplot(data = np_Intra_R, aes(x = var1,
                                                 y = AME, group=Office , ymin = lower, ymax = upper))

np_Intra_R_plot</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>np_Intra_R_plot &lt;- np_Intra_R_plot + geom_hline(yintercept = 0, color = &quot;gray80&quot;) +
  geom_pointrange(aes(colour = Office), position = pd) + 
  coord_flip(ylim = c(-0.5, 0.5)) +
  labs(x = NULL, y = &quot;Average Marginal Effect&quot;, col=&quot;Office&quot;) +
  ggtitle(&quot;&quot;) +
  scale_x_discrete(labels=c(&quot;County: Solid Republican&quot; = &quot;County:\nSolid Republican&quot;, 
                            &quot;News: Most of the time&quot; = &quot;News:\nMost of the time&quot;,
                            &quot;Post-Graduate&quot; = &quot;Education:\nPost-Graduate&quot;,
                            &quot;Outparty is extreme&quot; = &quot;Outparty:\nis extreme&quot;))

#remove labels
np_Intra_R_plot &lt;- np_Intra_R_plot + geom_hline(yintercept = 0, color = &quot;gray80&quot;) +
  geom_pointrange(aes(colour = Office), position = pd) + 
  coord_flip(ylim = c(-0.5, 0.5)) +
  labs(x = NULL, y = &quot;&quot;, col=&quot;Office&quot;) +
  ggtitle(&quot;Among Republicans&quot;) +
  scale_x_discrete(labels=c(&quot;County: Solid Republican&quot; = &quot;&quot;, 
                            &quot;News: Most of the time&quot; = &quot;&quot;,
                            &quot;Post-Graduate&quot; = &quot;&quot;,
                            &quot;Outparty is extreme&quot; = &quot;&quot;))</code></pre>
<pre><code>## Coordinate system already present. Adding new coordinate system, which will replace the existing one.</code></pre>
<pre><code>## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.</code></pre>
<pre class="r"><code>## Combine D and R 

library(grid)
figure &lt;- ggarrange(np_Intra_D_plot + rremove(&quot;ylab&quot;) + rremove(&quot;xlab&quot;),
                    ggplot() + theme_void(),
                    np_Intra_R_plot + rremove(&quot;ylab&quot;) + rremove(&quot;xlab&quot;), # remove axis labels from plots
                    nrow = 1, widths = c(1, 0.001, 1),
                    common.legend = TRUE, legend = &quot;top&quot;,
                    align = &quot;hv&quot;, 
                    font.label = list(size = 10, color = &quot;black&quot;, face = &quot;bold&quot;, family = NULL, position = &quot;top&quot;))

figure</code></pre>
<p><img 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hihwjXIRrqCVmWigw46yMc9+uijG6Q4/JQGPY8//ni3xRZbeJd9s88+u5t//vndBhts4Pbbbz+vgxh+VXtHJDOE96X0Go3Cgw8+mMbVACwBAq0QYAp8K5Q6jPN///d//qWhZOSb87jjjnPbbbddTarqUKywwgruk5/8pFN8WXbKglNT01dffXW3zTbb1MTXjqaxK+jloynz2RDO67imyktYVQjWGVKy9n0wxd24U3/uBh64vxCKwctNyLOPwdCmmxe6rszIah9SZjcKf/jDH5w+cmpPcougdhaHO+64w02bNq0lpV98Xbx96623us997nPuU5/6lJM1cavhqaee8uVfddVVW71kWDwpIuVSQh/lF180qbeN8MQTT/gBBKWloOdv++23r5tSvfqWUZa6mXKiLoHpM152V1y9nXv+hX/VjZM9kSRD7tY7vmUDQYNuheWHvzuz8bu1Lzcoeob1LIz1EN5FWbcu9eqlgZlwTb048XEN8IWgbV2rgZ6RDI2e8TLepd2si9zg6Dvwj3/8w2cjX+NLL720f/+rA6Hvg9zpqAOd9x5/7LHHvBwidzsaNJUf8RVXXNGn9Y1vfMOdeOKJfnvhhRf21oBSqpbBpIw0VLCq2oyHEv27ddpkt8e9v3Yz7B3UanjyjRfcZ+8+3Z276pfdkrPN2+plXYl3ySWX+HYkuUFBSvRJkyZ5P/TyRS8FyF/+8hfflvIshFstlO67BlNkLTbaQtG2FN7zefWQb/4Q9Bzp+ZOBwlgLWstA7+Tnnnuu0qI/8YpzO183zk1poryKC/nKzAG3582D7tR1htwHF6x2UmS7z1eQp9/znvfEVRvx7VCOrFzfzvdjpAufWNt58Qjnhooow4cG3CtnWEkHEzfLR0a6xP/LT99wPX8f+MAH/newzlaIq0GmVoPe9xqsueiii/wlmjWitUteeOEFd9VVV/m/c845x11wwQXufe97X6vJ1o2nwdogHzYzSnnttdfSuKEfVzdhTkDgLQKDkOguASkZZVmhoE7DNddcM0z5mS3BVltt5TSKGhZD+tKXvuTyRsHDqIg6HHkhnNe5enHyruunY4O/v7iw8jPwGfzzn9zAv+pPXwvxRuL3pptucn//+9/9nzoO8jH7u9/9zivGNWr3y1/+0lvoZMsiKw5ZJ6sT02741a9+lVoaF0lDgprylgK/3SDraVlPNPtANkr///2//+ctWDX4oHDSSSc1iu7q1beMsjTMmJO5BG6+9cBCys84ESlBn51yR3yI7REmoMGG8O5q9LvHHnuMcMmGZ9foGS/jXTo8x3KOSO7QzAApPzXQKvlCHRp1hCQnyKJVg1iaziwljCzis+Fvf/ub9zWuhRYlj2jWiqxJFX7/+zf97epd/N///tfPBlC8MpiUkUa2LlXty6/n3veeVUj5Gco6beZrbq/7zrRr3xyoC8dH8lftR9Y/ajuyIpKVjrbVHjQge8YZZ7j55pvPnXzyyf7ej2TZxkJe+++/f827TrO09CzuvffeXukpg4cf//jHY6Eqo7KMmpS11y3FlJ+hIuYEy+1zy6B72nyGVhV64fmqJ9e38/0Y6fvw8mkFlZ9RAV/5pXMzHo4O9Nim5AMpP9/1rne5yy+/3L1u62Xo/S8dh2SBbbfd1smYRLPoZMFMgMBoJ4AFaJfvkASahx9+860oISdYTDTLdr311vMKK1mLqoOiKTcycdeIuFaOVwgjHVpYSYKUgkZkNBqiUZnYAlTWH2EamixKpVyVJZvCIoss4hZbbDG/nfdPyjR1TPVyU6dmueWW80orTb1vFFSGO++80/9pWyOC7373u9PFlxpdOyLnjNvAtZ1NO9LiSDNXMCsY41llEFu5NMgGWQ7Lj6xcIMhnrDovQdGnuKEDm71uJPY1tVF/VYdf/OIXvgg/+MEPvJuIa6+91j9jK620UtVFI/8mBKY8d5d7cPK5TWLVP52YFdZtfz/UfWyDi+tH4kxXCUhhstpqq3U1j5FIvMp3aaP6yYJO09M1SCQrfQ2GZf01vuMd7/AueTRIK+WVBoE23nhjF6a4K30pNhX0zpbFXxzCuS233LLGeq0MJmWkEZe1yu2TH7vGTZ1hZkZthv+88rS74Knb3HYLrdVmCp1dpg6wBlTly17fyzjIl+xnP/tZt/jii7sNN9zQnX/++e7nP//5sLYWX9Nv25K18951H/uYLc5nU0nlFuS8885zBxxwQL+hKaW+v//vgLtzavvWsy/NGPB+Q7/37tats0sp+FuJ9MLzpe+D/rIhfCOKfD+yaXRzX4sbTb+p/bbjzIr41XMSN9dB3SxlNWlLp/DHP/7R6w00qzAsoBxKo0HV3/72t+4jH/mIU/9J7/68WSQhPr8QGA0EUIB2+S5oaq6COnmHHnpoodxkiaHrJXDKtFwKUCkysybuF198sdOfwp577unuu+8+9+c//7kmr7gjI4WqpiaFKfH1FkHSyLRWlM8bzdHUuR/+8Ic1yrSQocr705/+1JdXo0TZIN+mJ5xwgqva6fPgDde7gbemPWfL2Or+wFNPuoH//Nsl71yh1UtGPN6HP/xh953vfMd/kGRNLN+g8t2ioKlZUqQvv/zy3pdsXDhNZZGPWgku+uBJISi/cSHoOl0fpjwpvqbAqa1LEa92pmNadEv5aZqELIxk8akOuBT3UsqrM62OQTYorhTvmgYhC2ZZMCndEKSQlwWTgtqc8lbQIEOwnvYHGvyTcl/tW/XXlE0phzXooI6bpoPGoVF9VdZWyiJlhJ5hDWJI2SDFtaaSajsbQhyVbfLkyV45q2dffvYUNOqqeup61V/PvSyB3/72t7s11lgjl2nIQ+VVvTVAoSnLGpiQEBMvhhLqq2vy2oeOKx2VQ1P2qlAY//v+N5XXKku74elnbnRTn7/HTZrnzem87aYz0tcVaUtx2Zo9V3FcCb5qJ2pbGsTSM6J2MtpnFMhSU+8OWTbqeVH7nnfexlOHm3Fp9r5p9C4tcq86ea7jexe29R7Tu0TvBckTWeVniKdfnZdrAll4yFpNcoMGXTWIqymMCuFdq3ePBt1k+Rcs8PW+1/tEA6Xi3YiJ0mrGXHGapaHpyIqjKbjKUwO0aqOdTL9WvmWH6UMz3XlP3dJxsmc+eWNlClBNbVeQv896Qd93yYf6Zun7mjfDo5X7Hqevb5G+77fffrv/rmmKZVjEM44Xb6td6FsrQwAp+PWtlWzSKLRzTaP0ipzTsyYFqOQdyc15z2mR90jIu8g7vJnMEdLUO+GKK67wu3JD0oxruK7bv2eaz89OwwWPDLiDrHnPMVwk6zTppteX9XwpI8nfkt9lnb3WWmv5d2Ij1wpFvpmN2klWru/k+9EUWIkRXr+888Rm/GPAzXwyceMW6jyt0ZSC3rvyranvq/pzeUHywG677eYVoNIdjDYFaKvv9meeecY/M6qr5Jhs0PdE7tZkNJZ1Kdesz5xNS9/BZv2w7DXsl0jAhFlClwjYw2ATMpz/Mwu8tnIxJ+lpGtYJTUxhkZhjYP9nLxx/zoSP9JhZSySmBPX7NhKfXmvCXxrHPnSJTVtKz9mLaljZTEGZhPRNKZKYQiX5zGc+k1jnIr3OPqaJjfTUXGudocRGgdI4prBKbMGF5NOf/nRiCpL0uCnUEuu01Fzb7o75JEva+Zt24P7Ji7vs2PHfC6ed0lb+7dY3XGdT3VOeNg0hHM79NWEosQ6hj2+Wv2kc60T4Y9bBTY9pwxxap/FDG9avWSok9iHxceP2HcfRtQpm/ezTNmuixDosaVnN0iExoTwxJbk/ZgsE+PjxP3MbkZiCPL1G6VtnO7FBgcQ+Gj6qTYmtOR/KYB+nOKmG28pb1x144IE+nk3T8fv2YUvsg19zbaP6tlIWGzlNTJE4rMzWWU9McVmTl3Z0v/ScmE+dRM9gqJ8t/OLj2sITiX2gE1NMJNYhTc8rnp5NsTJlxLB077777sSsUGri6xqlofdLCOJsyiMf7ze/+U04XPNrgo4/r/dUJ6Gd51fXnPKLhZNjT5rQ8d9frju0kmc4PBd6FoqEom0ppN3KcxXi2iJ9SXg/hLanX30X9I3Ruz4OIY7uSyvBFkzxbcesE1uJXhPnsMMO89faAF3NcbV3W1QtsQEFfz6USc/Pt7/97cQUCDXxw04rXJo944FV9l1a9F61+1yHumR/wzMc3hvZ89l96+yk7EwZk5jlR7ofeOrXLP4SG0TNPWeDoz7Zekx0shXmilcvjUcffdR/j+IyhW1T1CdmiaLL01CvzaQRCmy087669KFbkoUu/XIpf3c/8WAl7yt9J8V4lVVWyf22BIT6BpvSMuzW/LZ63/UcKC/dR5tWW9PO9A6yQd2adMOODcgmeqfE30ylo31bFNLLHiFu+C16TdG21Mp73nyP+zraDK1QrJrfou8RXVz0Hd5M5lC/wQbQa2RDyRrm+iIxBa4vv800qil3OzvtPF8PPDk1mf+Macl8Z7zY8d9v//n8mHu+zHWC529TlZMf/ehHw76B+g7YoMSw29HON7NRO8nK9Z18P4YVtoUD7bSd56ZMTR7admry4Cee7/jvibPa64+2ULWGUWx2h7//NtO0YTydNEMPH1fv4laCDX4nNuPTX2OLNde9RPJVtt9UN3KTE5KjwvfcjHAaxrbBsTSuyhqHst/t4T1nrtPibFrqM8cXtNoPi69hu1wCFYxxWZPukyCrmRBkhdJOeP/73+9uuOEGf6ksHD7xiU/4UT0dkN9Gja5pWrNWlM8GrZArKw6FSy+9tGaae6MFY2RNoRUdNTqh0R5Zn2pKfgiyMttoo438KIicImu6k3VS/Gn78HpLP+3IZ5v2w6JLOmaKFD99SqMo8v8oS5NKglnMObPeLCWUlU4phclPRKNZcn0gh/savZWFYL2gdqM/WXtpKpasP+VXVFMc/vSnP/k2qH21Py2coekOf/3rX71lqdqF8omDrIFliSWrEfmLUd551g3hGlk6a+EmWXvKylgWPbJo1uIKcimha02Z4a01NQqn+Na5SVeEbpR2yEO/enbsY+4PmXLf/2pVVlk7alRO7V5+b0JoVF9ZPTUqi3zwyfLWXt9+gaVNN93UP18XXnihX3RJz7lGTfUsxUHPqRY4k6WVrNhkHaKpqSGoDrIKVbqm+PHT6GRpq2l0YiVLHE1XDEEWAXJ8LqsQuUfYeuutvRWp8pZvU1kKaPVm3SNZC2hKo1hrhdG8haHkP1Uh8Av5jMTv669Pda+9/mwpWb3wos1/GiOh3bbU6nMlDGpLmrEgyx75YJSVj0bG5etRViqyFJQ1ePi+jBZ0WlxQTvi12KBWJZU1uN49soJUnWRFJt/IcWiVi6zDGz3jcZphu917VfS5Dvllf2VJJks8hTDjw+80+CcfX3JvI2szve81Y0Pveb1XNNNE7xuxkNW62oes+PWdUPzvfe97/rvQbHGqVpnXK6aslWTRZ51dt/7667uPfvSj/nulVYbVRh944AH/fpOcoanZoyE8+OozpRVDaS06a60bgtISb5CQvheSDeSrUN9z+YrV9G19M+OgVeHzQjv3PUwP/+pXv+q/Z/pm6xul95NkBL2fQtB3UL7o1FY1q0RupzRDQjKP2rDcKchdlM7r+6bQzjUhv7J+ZfWtWToKscwR0m/nPdLuO7yezCFOkvfVl9A0fsn3kvslw8gfbKtyV6hT2b+TX7J7aX48ywgPvFhGKsXT6PT5Uo5ydSX5TvKm3otBltUMJVljyyo6zABT/Ha+mbquXjuRLBkHfYPL/n7E6ZexnbxgbeeVctrOzMfLKNHoSkPPufod+v7bwLXvq+g9K3+fcVuSTJA3m62q2ozUu73VPnPgUKQfFq7htwsErIEQukRAo692y/yfRuTaCbLoDGmYH9CaJMIIuUb88kIYEdT1spaIQyML0GCZo+ussxtflm5bRyctl/lQ9Mets5HYy9Aft5dlGje78etf/zq91oSn7OnC++2M+E01q7kyrD+VxrSDDqhktLiIBaigmn82z92E1ZRxnnWNLIx1760zmcbThk0BTUyZ7c8p7xBkDab4sviMQ7AA1bljjjkmPRWsVbMjxYqg0UXFl2Wj8ouDLAt0TmUwBYE/JctWHdOIdNFw+umn+2tN6VdzaSi3rJ7zQr361iuLnrWlllrK52UfymFJBmsSWVfLMjYE1Ul107Wy2lYI7LQdnn9ZlWZHXWUBo2tleRKCLDqDJUqe9Ux4X5gyNlySaBRW1jYa/TUFWHpcG6qXrMBkmRvKVxOhwE47z/DkyTd1bPkZrEfP+7+NKnmGw/1o1QK03bZU5LlSG5OVttqPrsuG0LZkRRwHxdef7mUrIXxnZK1nwnTDP1O01CQZnpnYAjRYmujdYUq5mvh6NsMzaEq99FwRLrqo3jOuc9l3abv3quhzrbzrhdgyopkVRZxGsLiLuQdZxNyoxFH9dvjuZ7lnmShyUeZ5aYR3VZ4losoQZq/Iqi6EvDYTzhX9bed9ddid55Zi/Skr0l/cd2Ul7ytx0vNjg4H+WQ/PvA2G+BlCNohW9/kvet/Dc6BZENm2q9kNylsWyHGwgQ5/XO8UzdiIg2ZK2IC+Px9kVp1v55qibSm85/WN1wyp8GeDkd6aVhbrslA11ztxkf12O++Rdt/hjWQOyS5iLgvVrLyhWWShLVRlASqrzTKsP5XG1659Ycw9X+GdqPug/lkcJLtpVpPO6TsZQjvfTF3bqJ3kyfW6pp3vh64rGtp5Nz9zRznWn7IgffSQ3rMA1T3QjJ/QhwzPug16JKYETWwwKncWW9F7F8ePLUA1k9TcLtX9C/KqyhVbgHbj3Z5nAVqkz9xOPyzmwnZ5BDp3mGItjpBPQJYnIVjnK2wW+o39ZFrnq9C17USWJZtGeBXkOzK2/IzTk4WoRtc1sqdV/xRkQSZ/YAoafa4XNIKkkXsFXVNJMIs6SQOlhFFiYdKsLqEtBV+V9eKrDSjIEjgO8ociy0/rWHhrxPhco235C5S1bwiyZqwXTCDzp2yq2jD/K1rMSVYHpoB0sgDqNJgC1CchK8c4yMrBOtDe2lIjdZ0GjYjLCkt+6eRTNxvk61eWmnpfXHLJJdnT3roi+JrJYyermngUVgloMRIF+fMLQVYksgCWVbeuyYYvfOEL3tJUq3UqnoKsf+XYXNZdsgCOgyzprKPl8wrli893e3vCxDetzsvIZ+KE8tIqozz10mi3LRV5rqxz662RZEEsS79s2Gqrrfyh4Ps3e77ovqz11JYa/dn0z6bJhkVZTDmRfl/CRbKAlyWjgizSQyjCJVzT6m+79yqk3+pzHeLn/cr6R0HWWUX8tsoKVEH+48oOZTCXNfz3v/99vzikrP/jINlCFvUKZbXROP12t+caV/+7VzTNMtMqmresyuQTTt8yLXikYJ1VL8tpJoD4y5rRuik1Sbd73+UXM+sjXJZyId84E/nuVpD8KSvwOOi7p3eDgix2QmjnmnBt0V9942V9Gv5kTS9rWslcplTy8rNmXsWhnfdIp+9wWXSGb3qQOcI6A4cccsgweUMyjHwMVxnmarwma6GilZlWoYwtcrvPV8hH7Vz3KA6y1A99OT2rIbTzzQzX6jevncTnx8r2wJtLIpRS3DLTKqVAJSWimRSa7adZX+oT6H2lGSZa9Ejve80w0ayMbL+xjOzV55QP9np/mp2UF0bq3V6kz9xOPyyvbhzrnMD4zpMghXoEguCg8zaKWy9aw+Nh4QFFUieu20FTcYJyKbvYUpy3FC5m1Rcf8gocHVC9NT3SRuJqzsc7mj6ll1qsJI7Pd31bnaa553a2skfHWSWTqhX8Wq1AaEtSZDYKUnBoAREpG8844ww/5V3TH2yUy09Hb3Rt3jlNQQvTzfLOx8eUr4KmaWeD0jjxxBOzh9va10daij4pBrQychwkQGoqv6Z/6gOqxZc6CXqmFDS9PdtZ13FNN9Vgg5SVIa6OhyDFaaOQd16KS4WwOIm2NQ1QQYMxclmQF/Rcmo9YL8SY1YqPYn5S/YIHmgZv1n/pZWH6e1aBnEbo8sass8xviupZzJXA8IXWimY9xxyLFb2kkvihfRRtS0WfKz37cdC7XMo0ddY1xVghCH1xvHa2NVVPi7M1ClkFf17c0L71jEnIzIagAIwF9KJcsmk22m/3XoU0W32uQ/y8X3VSFDSAoc5KLJPkxQ/HwmBrGDQLx8v4LYO5pnLGi+tokRy9P+V2SIM3UqorlNVGy6j3IrPMU0YyPo0y02qnUFpUSIPf+tPzdOWVVzq5H5BLI02NlaJR3xG5rZHrGIV273vecxBkGOUVgu51kCc1bT4vhOOKpzYjJW3Ra/K+4Xl55R3T4G08qKTvs+QytV0tvijDAg006h0rpZVCO+8R9RU6eYfnMdeCiQpBkeZ33vpn1rp+0CF8G+JzI7W9iJ98Vk5ui8xWq7wvJ9XWU2nn+Qqp5907nct7Ztr5ZoZ89FsvrzjOWNge1KtZS2oMDXRc3ME3H9uO0xmNCcgwxNbz8H8aZJHyM7z35VpB8qHcaKmvYGt/lFYFpRuMpvIS1Xc/+15q53vQ7ru9SJ85PHNF+2F59eZYZwRQgHbGr+HVYXRckWJ/oA0vypyU388Q5H+s20H+vEIISpSw3+w3WJvI/0x29L3eteqoSAhtVUFWL512jicrrOQGbrqhnUtrrknMx81YCKFT2MwfrSwu1LGQpZT8ZelPHRqbZuD9quVZZDSqf7P8wrXqpMtHn0LRthfSaPVXil0FdYKk9MuGMGAhn7WyFulEERA6MPrg1QuyAFUInbE4XjN+QbETXxMUH7EVTkhb/tOkcG0U4veOPu6yBgmKBfmGlW89CT9SFmf9ljZKt8xzg4Pj3MILruf++8QVHSe7yEKNeXScQUkJtNOW2nmu9FxotF9W0hJs4+9C2T6e9FzIJ24nQYN2QWknn7mNgrmD8cpACfPdfN+0c6/icrf6XMfXZLeDDCKrMj3Tee+67DXaD89/2TJHO20xr3w6JksmKeCkfJN8FQ/2lN1G65WhyPH3zb2sGzQfhUMdzj2Zd8KcboU5Gg9iFilXp3GlBNGfBsck+x1++OHed7eUYeoISxHXyX3PkwWCvBh/3yTfKB8p4+oZC+h7pQFHtRW1H8Uvek2z73Ejnro2b3BX18hXtwYd5WNflnnBSrXd90gn7/BsHfW91ztWAyhBMZutZ9nvimz6zfaXnMPkRlOCPlKCL8f1FqhWARrXtZXnK46f97zofPaZaeebKYOBOGTbSXxuLG0P2DjheOvGzXiz+9FR0Ses2tHlbV8cBon1Dm4WbJq4jxKuaRY/77zW9tCaJPrTWh+aLSf/xVJWajaZTRVP21ze9UWOybpc/c96Ic8CtJ3vQbvtuUifud1+WL26c7x9AihA22fX9Eot3hJCUA6G/VZ/Q0dE8YOSpNVr24kXj6gXndISLAyL5KsptOq4Nnq5FUmvSNxkzbWc61ABak4QXfKuVYpkW0lccZYQq9DsJS9BSR80LTqgBUVkCalRPn1kZBEpp/dalKjVznRWaGoEQEK7gjol3QoaGQzWi+ZfLtdKSIKBBAm5dJCbhryp662WTx0uhUZ10v1RCHH9zlv/mvGTIqeVEDqLUn5KOGkUglsLxdHUF1nJnnzyyb5Dq8UnzI+vH7jYeeedc61aG6Vd5rllltquYwXo7LMv4hZc4M0ps2WWrRtphfZRtC0Vea7UTuQGQtZbCurYympJC1/IClzPTJhi3I06tpNmaNu61lYk9tOzWkmnCJdW0ovjtHuvQhqtPtchft6vFqCQwkLfWLkvaeWdPWXKFDfZXHYoaFG4skMZzLUY3Prrr+8XstNgjxZtU9vUFDy932RJF6bsll3+dtObZ8Ls7oOT3umumXpvu0n46zabbzU3ONDaO7+jjDIX26q1nqkGA7X4UV6QgkwKUN1jPYdypxQsEdu9761a5YTnTd93vQ+Cwicup87pTyHE13Y71+i6MoMGK7Sg6UknneSnyIe0QzmLvPM7fYdnZY6wkJjuoQZT8t5NoZyh3FX8brF44o67b6CjrNeab8gtXOKU6FYL0+nzFfJp9Xlp95sZ8tFvtp3E58ba9sQPdK4AHTTF+bj/dftHFMGkSZN8fkG52Sjzxx9/3J9u1bDjlFNO8QNGmg0m6+S8oJlymlkmS03N7tQ3Wt/kqkJ4H5X9btdMmmwo0mcOz13Rflg2T/Y7J4ACtHOGdVPQFHJNO5D1jKyuZFWmzmOrQZ2QMO1EHZmRmG4Qjx7GVj+tlFkdZSlBVVaNYrcaNGJfRUiWW94NvXMFN3hf+x2SoQ0/Kg1RFcUvlKc5SPedEllGyH9LK0HWEvLdqT+99KX01FRVW5DA+17LukBoJc1GcfTBkusE+ShVO8r6/dK18pklRaz8zbRrcSB/gmrbEhjkUyZYS2bLpulqJ5xwglf6dqIADQMhQamQzUf7wS9Tq5bTeWk0OxZW6tWUxH333bdZ9JrzEnykAJWfRilAtcq8QlXT30PhllxiC3f3Pce6qc/fHQ4V/l1t5QOtQ/emkrrwxSN8QTttqehzJYtvKT9l9SslUtZiSYMiCllfdSOMoiY7PctB0ScfarECvyZiZqeb75t27lWmeB3vyhJSq/zKdYj8Imq7WadV1v7qNEh2iaeZd1wYS6BoW6z3jtf7yBbC835dbbHJYSu9B9/Ho6mNit++S2zkrp16n9mAtmdhNse4WdwXFvtQGbeicBpiKr+0UoZpgKSR3BbcJ4VvXln3vVGh9SwrHw1cqgMepvzG12gQOHRANeiu+EWvidMreztYfYcyKv123iO2+Fep73CxUt9A/KQ8yXsugwxTNpMi6e32jiF31kMD7rk32leC7r9SrQ/WIvl3EreT56udfNv9ZraT11i4ZuJ6zr12aeKG/tt+25ltO7O0HfmxKY839N2lSG8UNJCiWTAKjWalxWlI9pffZw182AJb8amabcmMGmSV8lPv/ioVoO18D1SZMIBQb8Cp0RoarfSZO+mH1cBmp2MCFT2qHZd7TCSgByn4F5TSRh2LIkELwYTRBllajUSIR3eaWXTaSpru+OOPT0erg6AmE28JlVK0tPKXN1I/EnVVHkM77OxsSdO2sht6x3Iu+fAGbV07khfpQyRrDAUp9TR1oV7Qh9FW9XN6Sccjieo0a5rq/vvv7y8NinntBGuAYOFRL+1Wjq+00ko+WliIK3uNnK7bitHpwEDIu0hHV+1WQcqAespPndd0DgX5OLvmmmv8tv6FPLP1DcezZQlWV1ImBUvPNDHbEOewQERW2RTH63Q7lENK5DBdOE5T1q5SLMvqSxa/cdBxDWxIuNKUU91/HRuJQZm4HNltvTvWXedEN358/TadvSbeX2KxTd2yS38qPjSqt8M9LNqWijxXcm2goOc9rz3KklAhWFL5nVHwL7DR1P28IEWZ2rAWagmhCBddU+8ZD+nFv6E8Re9VnEYZ2wcccIBXVmk2STMZRG4uwsIBui50BsooR0ijKPNwXfjVwkaaZqegxReCdVo4L99kYcbNaGujy9vUdSlB2w3fX3ZrN2lCe++6dvMM1+m7INb6hslfZaOgWSMKH//4x9Nond73NKE6G2qrer4V5LomL4Tj8lMna9V2rslLt4xjkhvCYm+x5XU775FuvMODMkODoNmgmWMy8qg6zGnjmD9awyxU2xxg+NLyQ+49Fbn07/T5aod9aFtFvpnt5DMWrpHicg4Zts/S3uDUxPcnbmL5EyZaRhfcWsnHpGbp1Qua/Rb6dlm/mfWuCUYzcmkio5F6QYsmytezBl6rco0Vytbuuz1YxWoQLRskW6g/HYeifebwzLXTD4vzZbtzAihAO2fYMIX99tsvXU1RQqP8qbUStEpksLLSVPSgjGnl2k7i6OEP09GllIlHouN0JaxJQSvrQJnHKwRliDodQZCLrwnbulYvaynaOrGsC+l19GuWQzN338MlBa1QkyWXckO7fV7DRR1l362L9SHSIgRHHXWUX7Fdyi0pNffee++GWWpkX75TpMQOPqjiC4KiLnwQdS50QMuwANBqogrHHnts6p/PH7B/5557rrvhhhu8f6/wcQ0+bNTmZJnaLOijFpSrcubdKMiKTKsNK2haWgj16luvLJrWprQkOEiBHCtONcooVwPq1Gvl5VCvkFeZv3reNG1Uvp+++MUvDlPGSrks6z8pOTWlNBtkdaUQpj/GiqRs3JHcn2fuFdz66/7KTRhfzJJ84QU/5D7wvuNzp0qOZPmL5NVuWyryXIVZCprOlFUg6b3+wx/+0Be5FV9TRerWaVxZJivI2l3CZRz0btK0aA1mxFYPRbgovXrPeJxX2G73XoXry/pVfY8++mifnAbCttxyS29FH6evd5K46R2he67vc1Er8Ti9RttFmWfT0j0IPj6zAzVqk1p8IQwcj7Y2qrrsbhacn1m4mMuNAfMd+s2lN3Ufm2/lLI4R25eFdZDXdA9322231JIoFEJyhhTnmjmhwamwWrvOd3rfQx6NfoOC/4gjjhgma0seUhtXiOWgdq5pVIai59RWNai0ww47eD/bGmQJhhNKq533SDfe4brfKpv8k8YLyamPcPDBBzu5zhgN4YMLJu7w9wy58QPFFFmfXHLIfW3Faqw/xa3T56sd9u18M9vJZ6xcM34p5+ZUF6mgEnTCuxM3++7V1nKVVVZxsrzX87jjjjt6Jajex3E466yznOR8hZ122mnYwkJS7p1zzjl+Ibb4OvVRNHtQ51dffXXfh8p+WzV4uu666/r+jfqHQZEY0rnkkkt82mERoHC8m7/tvNu1aK/CRRddlC7cp33JSNtvv/0wmbhon7nTfpjKQiiHAFPgy+FYNxU9HFpMRgpMdSz0gpBw2Ej5IuWPlIshSMHYaAW0EK+sXyk1v/3tb3srL40kaRGUbJAPwCDwhPOqozrHmuqvzqZW3MyzNpQFXrCo00h85cGmTs3cz6bBnnOmG/z3fQ2Lk5gAmLx/XTe05daaz9cw7kieDCutKk99AGMlm47J+uKKK65wzfy6Bl8m+oBqqoOU8LIE04dUK7zKikhTCzQFLgQtiqOg+ypH2OqAarplO0Gj4JpmLysktQ11CuQbS1O6ZHUoAVyjkGEap341VV7TsqRk1DQQfWjDVLJsGdRu9RzKD2qYppeNE++rTet5UP5Snuo5rFffRmXRFFRNzdWvBAVZxgSLDw2KaPBAlpWynO5mkMW2PsCaxiyfjrpXylPMtCCM+Grhm9CBisuid5Y6OrKuUl3jTlocr4rthRf6oNt4o8vc327exz075baGRRgcnOhWWmEPt9rKB1l9R8cAhhTj8Ts/WwG1F90j3Z922lKR50pCnnwAa7qjrHylMNP9llWR2qgGBdRmNRouSwJNe+okqD6aYtUsaHrR5Mzoe3zN+qbcD+8OKfBkKS6fkJq6pXeXpkyrPjHnIlyUV6NnPC6Lttu9V9l0ytjXAitSGmrgQ4K92pIEfQ26aKaH7mfoLImb3gHdmplRlHm2/rLcUxtVZ07Pjdql5CrVQW5aZJEhazUpvFoZFMumPxL7By29iXunWYMeNfky9/yM2k5qNn+t+P6dZbZw605aPntqxPel/FLHVspMLSSoPyluJF/oG6zpgfquaZDwl7/8ZY2v4E7veyuVlSwqf8WSW7SgkNqJBn61kJBWV9fiR1o4S53/ENq5Jlxb9Df7ns+T1b7//e/7b3RIu533SDfe4ZtssomfSaT3p96jkhElZ8naVwPTmj0Wr1kQyl/F7yeXTNwSc8x0h9wxzk1+ufGU5rkmJG4/m/a+09LFFKbdqFcnz1c75Wnnm9lOPmPpmglm8/C2w5x7+RTrR5k7hYbB2s6smzk3q3WRq5r6HpdPs1zktkbvYr3XJNtLzlefR+/msLirZDjpJbJBA8cybNB1eoeEINlLxhHqL6ifoD5hsLBU31NKzWBVKiOOYGkfrtevBs/k5kHvtzAbID7fje123u0qv74Z6udoBpT0GKqjZAsZj2y88cbpjD2VuZ0+cyf9sG5w6ts07QNMGAECphDU1zX9s1HdxBSbiQnpiVl/JX/7298SU4wm9vClcRTfhI26pTMfTD6uKWly49hod5qWdQpq4tiLMD1nSo2ac9YRSkyR5M+bIJuYYisxS7U0jimgbNb4XP68jQYlNiUqPWeWJmm69pJLzBdoes4sCxNTkCb2cvVxTJBOzDInPd/uxtSpU5Oy/p6/8YbkheN+lkzb4wvJi7vs6P+m2e+0/fZJXvjFacnUe+8tLa926xuuM6uBlHXctrRt/n0SM7VP7IOV2PSWxD6A4bKaX1N0+TSy98F8ACbm0qAmfbO88emZO4eaNKxTkVinI7Hp5D6+KRb9ebPC8Pu2CEJN/LBjH2B/3iwLw6H0V23O/GHW5K/2ZB+hNE7YMMVuYkrQNG5enBDXpsj5eKbgD4ca/urZNKWHv8YWd/Bx69VXJxuVxZQ3iX1ME1NEpGW1AZLEFIuJCSzDyhF4muJ12DkdCM+/KTCGnTdBw+ehZywbzP9pss0226T3K7QdUyAnZuGXjV6zb4KPT9fcB9Qc73SnrOdX6dx19znJRZdum5x02rzJsSdNSP9+ddbKyVXX7p889tg/Rs0zbJ30tC2E+5D3a0JXDeKibSlc3OpzZSs4J7boXk3Z9Dza4FxigyuJCdn+nClAQtJpXN2DVoIpIdJr8uqcPWZCeJqsjez7a02oTo+FDVOMJXqu4uv1HVNcPc95oVUuurbeM17vXVr0XrX7XOfVK3vMOirJ7rvvbmv3zVbDx4T4xJTGiVnZZy9J9202gb/GlMzpsbChb4N4690Sh3pMFKdV5nlpWGcr0XfDlENpPfRe1fvVOi6JWS/742b9mphCzhepUZuJy9zKdlnvq0efeSL56d2XJBv/5chkkUu/kix06Zf93+J/2CvZ5rqfJiffe3ny1JRnRs37KrAxBXqy0UYbJaYAS/nr/psy1N8DGwgNUYf9tnrfGz0Hptj2+ard5QWbsZHYTKa0bPqWmpI8McV+XnR/rMg1RdtSvfe8njsbVE307f3sZz+b2GBN3fIVfY+08w5vJnOocDa4nYRnUvdcsqZNq03MYtzz1vu301DW8/XMlKnJ6Xc+n2zzhxeSRX81LZnvjBf93/xnTEs2uHBa8oObnk8mP1Ve/6HTeofr23m+Qn/PBrtCMjW/eu/rfuk9ng1Fv5mN2kk9ub6d70e2nK3sl9V2nntuavLUn6Ymjx42NXlwG/v7xPNv/U1NHv7i1OTxU6YmUx4YfW1H32BbhT2R7K/7Hf+ZX+TE/DgnNhici9IWfPXx1UfPC9IbqD9nStW0X6T0TRmamAVqYgNjNbqCOI0gT5oCND5cd1t90lB2ve8bBVPKpnElG2RDkXe7rrXZeokZHdTIF+p/6rum8qtceufFoUifWdd10g+L82W7fQIDutRuJmEECGi0XCtihulZjbI04c9pNCcehcnG16iErFpkpaa42aApzBpxVpBVhKxRQ5DPHhNi/K6sujRlKA7XXXedz1sjSQqyupCPT6WjURAFTZXXqJCm2IWgkSZNkT311FPDIb/YjKwENK06BE1j04hKGasJh/KEtEv5tal04w/czyeVLLa4m3nAm9MGSkn7rURkSTGag14Nuv+yBFNZZTEka4R6QdYVmuYtqwATkOpFK3Rc+WvUUG1Mo5CNgn1Q/MilpmqMRGhU30Zl0fMvixQ947HP3ZEoc5yHnlU9k3p+TDjJXTQijq9tU5x661G5Qoj9u2XjFd3vxjP84osPuQsvXdsXZZGFNnAbrH9O0WI1jV/1M9xuW2rluZIVuaaOK66snutZVTeFVFEEzVC47777/PdH7VvfsGahFS4hjUbPeIgT/7Z7r+I0ytqW6w291/U912IYer92asnbbtmKMM/mofeGrM5k3Sr/j7LQHYnQjffV8Y9c6U547Cpf/B8tt53bdP7VSq9KN95Xes40JVrfZ7WjVkMn973VPCSP6B0mS3DJnK2Edq5pJd2y4hR5j3TrHS5rcd1zPW+y5irbWrwbz9dVTw64L9745oyPXZcdct9Ypfzp7qPp+WqnvbXzzWwnn25e0422M/NR56YdNOCLPf69iZur2BqiLVW37Laj/onefZJT1G/T+1mz6WS5WUYI/QdZfhZ5v5aRd7tpFH23S78ii1fJv9JhNAtF+8xKL3BUu221H9asHJxvjQAK0NY4lRZLLyNN79VfnpNdPQCariZ/e806nN1UgKrC8kuo6b+aBh+mx+m4XqBSuppFXOovVMfjoCmHmgYvRY+mRIWgazWt+Vvf+pY3Mw/HO/ntxgfPlhL9nwJ08SXczP0P7KSIudeW/cHLzYSDECiJgPzKSoDS6rpSSjdShhfNshvPcKwAXXThDd1HPnR20WI1jc8z3BQRESDQcwS68b464dGr3PGPXulZjSUFaM/dXCpUOYFuPF+xAlSrxR+88v/6JWVVGHmgLJLtp9ONthMrQCesmbg5v9p++epdSdupR4bjEOgOAXyAdodr3VSlPLDpM07Op8MIuBShUnbKt6L8GbY6miorzkZBCzDpLy9IedrM+Ff+IuVnSwpMWVnIJ4ZGkWQJ2Gw0Xf4OtZiKFKdSgqqu8s8o34vKmwABCIx+Ahrd1UCIfmVNrlHlPffcs1Tl5+inQAkhAAEIQAACEIAABCAAAQhAYKwTQAFa0R2UklPKRP2N9iBLLzkF1l/RIEWpVpTOW1W6aFrEhwAERpaA+R3zi96EXLXYSFgJOBzjFwIQgAAEIAABCEAAAhCAAAQgMNoJoAAd7XeI8kEAAhCoiIB8uq255prOHMv71SXl9qIs/64VVYlsIQABCEAAAhCAAAQgAAEIQKAPCaAA7cObTpUhAAEItEJAC6NooTMCBCAAAQhAAAIQgAAEIAABCEBgLBOov6TzWK4VZYcABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgYARQgNIMIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgZ4lgAK0Z28tFYMABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQGEgtggMBYJvD888+XX3w9Fi+/9Ga6g+Ocs9Xsyw7zzDNP2UmSHgTGJIFuPMNJMuRef/05z2Nw3Cxu4oS5SmfDM1w6UhKEwKgn0I331asz33CvDk33dZ9T76vB8l30874a9U2LAhqBbjxfbww59+Kbj5ebTSJ9+Y+X4/mqvvl2o+2YKOmSt7qDA9ZuBsrvDtJ2qm86lKDPCHThE9BnBKlubxIYGHBuzvIVJr0Ji1pBYPQRGBgYdLPOOt/oKxglggAEIJAhMNu4iaaYmZg5yi4EIFAGgYk233HeWcpIiTT6jYCJkm7gbf1Wa+oLgd4mwBT43r6/1A4CEIAABCAAAQhAAAIQgAAEIAABCEAAAn1NAAVoX99+Kg8BCEAAAhCAAAQgAAEIQAACEIAABCAAgd4mgAK0t+8vtYMABCAAAQhAAAIQgAAEIAABCEAAAhCAQF8TYBGkvr79VB4CEIAABCAAAQhAAAIQgAAEIAABCEAAAr1NAAvQ3r6/1A4CEIAABCAAAQhAAAIQgAAEIAABCEAAAn1NAAVoX99+Kg8BCEAAAhCAAAQgAAEIQAACEIAABCAAgd4mgAK0t+8vtYMABCAAAQhAAAIQgAAEIAABCEAAAhCAQF8TQAHa17efykMAAhCAAAQgAAEIQAACEIAABCAAAQhAoLcJoADt7ftL7SAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0NcEUID29e2n8hCAAAQgAAEIQAACEIAABCAAAQhAAAIQ6G0CKEB7+/5SOwhAAAIQgAAEIAABCEAAAhCAAAQgAAEI9DUBFKB9ffupPAQgAAEIQAACEIAABCAAAQhAAAIQgAAEepsACtDevr/UDgIQgAAEIAABCEAAAhCAAAQgAAEIQAACfU0ABWhf334qDwEIQAACEIAABCAAAQhAAAIQgAAEIACB3iaAArS37y+1gwAEIAABCEAAAhCAAAQgAAEIQAACEIBAXxNAAdrXt5/KQwACEIAABCAAAQhAAAIQgAAEIAABCECgtwmgAO3t+0vtIAABCEAAAhCAAAQgAAEIQAACEIAABCDQ1wRQgPb17afyEIAABCAAAQhAAAIQgAAEIAABCEAAAhDobQIoQHv7/lI7CEAAAhCAAAQgAAEIQAACEIAABCAAAQj0NQEUoH19+6k8BCAAAQhAAAIQgAAEIAABCEAAAhCAAAR6mwAK0N6+v9QOAhCAAAQgAAEIQAACEIAABCAAAQhAAAJ9TQAFaF/ffioPAQhAAAIQgAAEIAABCEAAAhCAAAQgAIHeJoACtLfvL7WDAAQgAAEIQAACEIAABCAAAQhAAAIQgEBfE0AB2te3n8pDAAIQgAAEIAABCEAAAhCAAAQgAAEIQKC3CaAA7e37S+0gAAEIQAACEIAABCAAAQhAAAIQgAAEINDXBFCA9vXtp/IQgAAEIAABCEAAAhCAAAQgAAEIQAACEKc1woIAAEAASURBVOhtAihAe/v+UjsIQAACEIAABCAAAQhAAAIQgAAEIAABCPQ1ARSgfX37qTwEIAABCEAAAhCAAAQgAAEIQAACEIAABHqbAArQ3r6/1A4CEIAABCAAAQhAAAIQgAAEIAABCEAAAn1NAAVoX99+Kg8BCEAAAhCAAAQgAAEIQAACEIAABCAAgd4mgAK0t+8vtYMABCAAAQhAAAIQgAAEIAABCEAAAhCAQF8TQAHa17efykMAAhCAAAQgAAEIQAACEIAABCAAAQhAoLcJjO/t6lE7CIwsgWeffdY98sgjaabLLbecm2uuudJ9NsYOgWnTprn777+/YYEHBwfdrLPO6mabbTa3wAIL+N+GF3ASAhCAAAQgAAEIdIEAMmgXoFaQ5Msvv+zuu+++lnOWHDr33HP7vznnnLPl6/ol4gMPPOBeeOEFX90ll1zSzTvvvB1V/T//+Y978cUXfRpLL720mzRpUk16Dz30kJs6dao/tvjii7v555+/5jw7EIBAtQQGEgvVFoHcIdA7BHbaaSd39tlnpxU69NBD3WGHHZbuszF2CFx22WVu4403brnAAwMDbtFFF3UbbbSR23HHHd2GG27Y8rVE7E0C559/vttmm216s3LUCgIQgAAERhUBZNBRdTvaLsz111/v1l133baul8JN1+6+++7uwx/+cFtp9NpFm2++ubvkkkt8tU455RTPppM6ius111zjkzjnnHPcpz71qZrktt9+e3fuuef6Y8ccc4zbe++9a86zAwEIVEuAKfDV8if3HiLw/PPPuwsuuKCmRqeeeqqbMWNGzTF2epOAxpIee+wxd8YZZ3glqBShkydP7s3KUquGBO655x73kY98xH36059uGI+TEIAABCAAgTIIIIOWQXHsp/Hoo486KeUkg+y6665uaGho7FeKGkAAAhAokQBT4EuESVL9TUACx2uvveYhLLjggu6pp55yjz/+uLv44ovd1ltv3d9weqD2a621lhs3blxNTWbOnOleeuklPxVG91r7IVxxxRVu7bXXdtddd52TKwRCfxDQc7/aaqu56dOn4xKhP245tYQABCBQOQFk0MpvQdcKsMsuu7g55pijbvqaji33W5o2LxkkhF/+8pd++vVRRx0VDvELAQhAoO8JoADt+yYAgLIInH766T6p8ePHuyOOOMJ97nOf8/snnXQSCtCyIFeYzpVXXuka+VaSIlTTlg4++GB3xx13+JI+/fTT7mMf+5j729/+5hZaaKEKS0/WI0VAik/9ESAAAQhAAAIjRQAZdKRIj3w+Rx55pFt44YWbZqwZZ7/61a/cQQcd5OQPVuEnP/mJ+8IXvuDe8Y53NL2eCOUQ2HPPPd1mm23mE1tzzTXLSZRUIACB0ggwBb40lCTUzwTuuusud9ttt3kE73vf+7w/GC2MoyDFmRxmE3qbgJSjUnbecsst7mtf+1paWTlDP+CAA9J9NiAAAQhAAAIQgEBZBJBByyI5ttORAYaML2QNrEU6FTQFXoYYhJEjsN5667mdd97Z/73zne8cuYzJCQIQaIkACtCWMBEJAo0J/OIXv0gjfPzjH3ezzz6722KLLfwx+YY8+eST0/Ns9DYBTZP/8Y9/nI7+qrZaGOtf//pXb1ec2kEAAhCAAAQgMOIEkEFHHPmozlCLcL7//e9Py3jrrbem22xAAAIQ6HcCTIHv9xZA/Tsm8MYbb7gzzzwzTUcKUAWN/v3mN7/x2/LDc/jhh7tZZ53V7zf6d/fdd6dTVz7wgQ+4CRMmeN+Sf/zjH70/SfmafPvb3+5WWGEFt8MOO7i55567JjkpXJXG1Vdf7e6880635JJLupVWWslpFcSJEyfWxK2388QTTzgJTFLa/fvf/3byabrqqqv6P+UbRpbrXR/XQUJYyPfhhx/208FvuOEGX6/VV1/drbHGGr4u9dLKHtd0ck01f/DBB90ss8zi1llnHf+3xBJL+Kj33nuve/LJJ/220n/b296WTaLr+1oRXh0SrQqv9qER+KOPPtppUaxGQX6cdM/0J4uOeeaZx7373e/2fxpFLsJ9rLadLJ8XXnjBXXjhhd6KWs7955tvPrfssss6+WR973vfm43u98Xuueee89taDVVWEX//+999OsFH54477li3bSgftSP9yafWtGnT/L1Yeuml3frrr+/e8573DMtXbU7x5fYgBPmEDSuF6lgoSzgffmU1rL8HHnjATZ061S2//PL+mdVzq7qqPREgAAEIQAACWQJlyqCx7FaVDKH6dSKDxnUoW/5U2caCDKpyrrLKKr7PoG35B20WypQ/5f9es+DUNjU7Tsx0XxZZZBG34oorerdgMhRpFCSzaWEvBclCCyywQN3okrH/8pe/pOdlgZn12Z+efGvj1Vdf9XKX+iP333+/56VyS+ZW36KToL5TkAW1BoD6AvVCOzJuXlrtyK1xOt16bpBvY8psjxoCpiwhQAACHRA499xzE3ug/d9iiy2W2IfYp2Z+ABP7YKfnzC9PS7nYgknpNaasSUzRl5hCJD0W8tKvTbtO7OOdpmu+JxP70ObGfde73pXcdNNNady8DfNjmXzzm99MTFGbm4byNKVT8s9//jPv8vRYXIf//ve/iephfnDqprn99tsnpmRKr8/bsI9oYsrOuml86UtfSkzYSsxZfBonZpOXZqNjpnBO01G9TThsFD333DbbbJOmYQra3DjhoE1RSkwgTOPH91nbqrspyEL03N+Y+1htO6Fiag/mt6ohk4033jgxZXG4JP3V8cDPFKHJoYcemu6H46ZcTkzZmF6jDVO0JqboHxY3XBN+zbo7UbuOgym8m15nPrniS/yzax20htetvPLKyWWXXVZzHTsQgAAEIAABEShTBq1ShlBdypBB4zqUJX+qbCMpg9rimTVygRk+qAiFQixbmDK74bVly5+mUExM8ZnMP//8NfUIMpQNZCc//elPG5bJBpvTa21Kf8O4ajchbf1m5XXzx5meP+WUU5J//OMfiQ1op8fia83AJLn00ksb5tesbNttt12a9jHHHJObVicybpxgJ3JrnE7Zz436X3EbjBmHbeTb+A6wPZIE3EhmRl4Q6EUCZvGZfui+8Y1v1FRx3333Tc9JgdVKiD9CZj1WV6EZPiBSukrZdeONNyZS6oTjeb9SyNpoY24xzNIzNy8pWc0CrSZdGx1NbJp3bjo6GNfBrDUTGwGtuT6vbIqTVUiFDC6//PLEVsCsSUNlyh6TULLJJpuk8apWgF500UVpWVRnMc4GGyVOPvGJT9TEU1yzXE3McrHmuO5FI0V6zH2sth3xESezNK6pe2Bi1sQ1x+eaa67ELD5rsMYKULPOrokf2l62Q/D9739/WDyzuE3mnXfeRL/huvBrlteJjbin+RZVgNpoe6I0Qnr6Vd1ssaxh990sGZLzzz8/zYsNCEAAAhCAgAiUKYNWJUOoHmXJoHEdypA/VbaRlkE7VYA+88wzNXKLDATyQrfkz9NOO83LsEG+Ud/DZjHVyDs6t8ceeyQ2UyavaEkzJWN8UREFqAwTJDeGsknOzvYl1L/41re+lRq0xHlpu1nZmilAO5VxQ3k6lVtDOvot87lBvo3Jsj0aCaAAHY13hTKNGQJSgMTKEZsuW1N2WaeFj6x+bUpHzfm8nfgjJIWXrpP15HHHHZdoVNWmsiQ2ldpbf4a0ZZUZPug2tTexqSDJlClTvIXZXnvtVVOGAw88cFi2tnJkjXVlUHDqIy2LVpuG4gVAmxpSk5YUfHkhrkNQykrBedZZZ3kh16YLJ7/97W8TWzSoJj1brXJYco899liNcGJTaJILLrggkWWfyiZr1K9+9as16QQuVStAbfGrmnKdd955w+q36aabpnGkAPvOd76TTJ482cezKTqJTR2q4aT2ppH1vBBzH6ttR5bTsRWmuYBIfvCDH/i2r/uttvrnP/85sWlUKTdZ18bK81gBGiwQxENCqTqLat+yAghB9yl+js21hH9WlZeCLIvVznbbbbeaePvtt19Iwg8s2BT4RIrn0P6Uj46Fv1jQj626ZQFtU6ZSYVv5XnXVVYmstkNaYkKAAAQgAAEIBAJly6BVyBCqS5kyaFyHTuVPla0KGbQTBehDDz2UmGupVHaQDGEusVSVYaFb8mcwmpCFn2amhSDFrC3SVFM29W3yQjMlY3xNEQVokKnUJ5Fi++WXX/bt7/bbb09i2VHxNICeF5qVrZECtAwZV2UqQ26N61bmc4N8G5NlezQSQAE6Gu8KZRozBMyvZ/ohr2fhaX4o0zhf/OIXm9Yt/gjpA2y+b4ZN51AiUoKGD3n4/dGPfpSb/uc///k0rvlOHBZHCqaQhkZqJQjkBSnkpBwKcc2/qBcesnGzdfjoRz+avP7669loyWuvvZaYX8Q0vXe84x3D4nzqU59Kz0uYkvI0L5i/1TReKF/VClBNwwll0a+mGcUhnmYvZZn5XY1Pp9tSnMVKXjHLC1nuY7HtHHnkkSkzjcrXc9tgPsISKcMD35htVohVm3/llVdSZBKW1ZZD2GeffdJ0PvnJT4bDub9f//rX07jqXGWDOqShTOYDK3va78utRYhj/kTrWkDIqjvE02+95zI3Ew5CAAIQgEBPEyhbBq1ChtANKlMGzdahE/lTZatCBs0qQGVRJxc6eX+SiWWsoMFXWQRqCncsN+y6666qxrDQbflTcmo911aHHHJIWkaVN+seSIVtpmSMK1RUAaoZQPGgeUhLsrZcLwV+UpJKYZkNzcrWSAFahoyr8pQptyq9sp4b5FvRJIx2AihAR/sdonyjloCs0WyBkvRDaSu955b12GOPTePICq2eQBAujj9C8gkpwSYvSIETW61JORgs1rLxZXkaPuia0hsH+UiKpxUff/zx8elh27IGjQUsCTLZENdBaderg66TX6VQNv0q/RCk5AojyTr3pz/9KZzK/Y3zVfyqFaAqZOzX83vf+15abglV8TTvesrrcIHiSxgLrGRBmw1x/cdq24n9MknZ2CjIciDwiJXCsQJUiuV77rmnbjISnOVuIKTTzL+tLDVDXP1mn+dWFKCxzzb5pmoUJIxvsMEGXij/61//2igq5yAAAQhAoE8IdEMGHWkZQreqbBk0rkMn8qfKVpUMmlWAxjJHkW3NLslze9Vt+VNlrDdTSVzVf1l44YVTWSrPCrSZklHphFBEAao+RZ7v+JCW+iDBclj1kHujbGhWtkYK0DJk3LLlVtWvrOcG+TbbWtgfjQQG7eEmQAACbRC49tpr/arNulSrHZqgkZuKVps2Icyfs49WzYrxuRdEB7UKpym9oiP/29SK8lqdPQT7eNVd9XDxxRcP0ZxNjXcmfKT7WmlRKzUqaMX43XffPT2Xt6FV5w866KD0lCkl0+28DRMU6tZB8VdbbbWay0z4SPcvueQSDdL4/fe9733ORvLTc3kbpmDMO1zpMVN6p/kHzjqgld61ariCVin/yle+4rfr/VMcm4Kdnv7973+fbudtjMW2o1UobfqWr47a9wEHHJBXtfSYWSM7cxngTBnsTjzxxPR4vGEDA84UzfGhmm2zMnXWAfOrxJtFhF9ttCZCZmeppZaqOWJWzDX7rezEbd6mYPnV6etdZwMr7oorrnD6NSVvvWgchwAEIACBPiLQbRl0JGQI3a5uyqCdyJ8q21iVQSX3SC6ymVHOBnhVlZrQbflz8803dzb7rSbPeEfyna2RkB5qJs+mEUvY2HbbbZ25FKqbkvo4sTxuCyLVjVv0RFkybrfl1k6eG+Tboq2C+FUQQAFaBXXy7AkCNiqY1mPLLbd0+mjmBbO4dGbllZ6yqbrpdrMNmx7bMMqkSZPS8zaqmG5nN/SxjINZiqa7Zh2XbkvgDcra9GDOxkc+8pH0qPk9TbfzNrIKo2wc8/HoFcjheKwktCk94bCzaczpdr0Nm/Jd9z7Uu6bbx230Pc0iZmH+e9Lj5lu1hkF6IrNhfp3SI/H16cFoYyy2HVuwIK2BWVc7Wyk03c/b0LP17W9/29lou1tllVXyojhzq5B7PD4oJbWENvMPGh+u2bapUc6slZ2t6FlzPH6Wak402DH/SC60BXMN4bbaaiv3oQ99yNnCYs5WJ21wJacgAAEIQAACznVbBh0JGUL3sZsyaPjO1msvjeRPXTNaZFDzB+4kJ4Y/W1DImX/zmmppkFwyhE2J93KE5KJ6IZYfuyF/brTRRvWyTo/b2gXpts1uSbe7vSFZq1mQwUUINoMtbHb8W6aM2025tZPnBvm242ZCAiNAYPwI5EEWEOg5AlJq/e53v0vrJUFEI631QmypKQWHPoJSNjYLsYKzWVxbDb5uFJsqX/dcLHw2++iFRGJlq00BdjZNyNl0lnC65ldWpc2ClK7BKtWmdaXRH3/88XS7SNlkUTAaguokBVcIyyyzTNh0sQBq06q99W16ss6GTVtKz8TXpwejjbHYdiS4h2ALG4XNjn7rWVDXS1T3S+3HpsI7W3TMSfiVkl/bedaewUK5Xnr1jp9++ulOVttBQW4Llzn9yep10UUXdTaN333iE5/wv7KWIEAAAhCAAAREYCRk0JGQIVSXbsqgncifKttokUFt4cdcGdsWyXQHH3ywlx00GKtZULYgqjPXOSp+3RDLj92QP1vhHst45qPdmc98X/a6hS7pRCt9iXjWnGQ/yXk2db7jEnRDxlWhypZbW7l/9fptKg/yrSgQRjMBFKCj+e5QtlFLQMrOoLBTIc3Pp/9rtcCyAm1FASpBptXQSMnZKI0wDVtxWhEMFM98gDqNPmpKv8IDDzyQK5zpXBEhWvHj8PTTT6e79RSsaYS3NqQIHi0KUAmWcainAJXw98gjj8RRm27LlYHcBZivoty4Y7HtxPc7FkBzK9jiwVYEOSVlq5M683/rp9KbQ/66qcua2lYNrXu+1ROyotZAiKZjxc+grteU/NNOO83/afqaXB/88Ic/bMk6u9X8iQcBCEAAAmOTwEjIoCMhQ4h+/P0rWwbtRP5U2WKZZDTKoO9///ud3FBpFpp+pRi3xVadDAm+9KUvqQq5IVaAdkP+bEXuyhptiHWRNpdbsRYOtlK2WP5UX++pp55yCy20UAupN44St6c4j8ZX1T/bLbm10+cG+bb+PePM6CCAAnR03AdKMcYIxFOP2im6rEd/9rOfNZ3i265Ss0iZbJGYNHqehVt6MtrQdOA4ri24E50tb1NTnENopJQKcfRrKzvGu5Vu2yJMaf6aahULfLGl64orrujks6loaGR9OBbbTtyOYsvZolzi+OPGjYt3c7dt4SK3zjrreMVjNoKEXk2Pl88oCXXqcMTuLjqxCtC0Nlm/2IIHzhzH+w5MdrqVLKz1rlBbOv/88711aLaM7EMAAhCAQP8QGAkZdCRkCN0xZNDO2q1miFx00UXejc5NN93kE9trr72cpsl/+MMfzk282/JnbqaZg5Jt4pB11RWfa7bdSBbOXtuKbBkbuEiGjPsi2fSK7Jcp41Ylt7ZaX+TbVkkRrwoCKECroE6eY5qAnFjffPPNaR2OOuqomsWI0hOZDX3sJZQo6AN8xhlnNF3kJZNEV3Y1Rfiqq67yaU+ePLmlPGShFvs+XGCBBVq6rmgk+ZIJvoH0sW8lFLWkbCXNduNccMEF6aXypxV3KFS3EHTurLPOCrtj5rfsthP763zsscdGhIOsmOX7U21aQYpqPaeagi7FZ9bPVrxIl+IXEbwVPy9ocaOwwJEWgZIlhxZk0rS3IIirY/P1r399TLaTvDpzDAIQgAAEihNABn1zlgQy6P/ajpTI55xzjvcRqr6G2Hzyk5/0fZV45lG4otvyp+TwRgsNqRyxrC4lY1bWCmXVb3yv4+NhO6tMDcfzflsxpnj44YfTSzVVX3JhGaEsGXc0yK2t8kC+bZUU8UaSAArQkaRNXj1BQIrLEDTC2myl6hBXvxJQ5LNHQas677///qX4lfEJtvkv9pHYqgI0jielXiPBpc1i+cvisskPT7MgZVHss6lZ/G6e1zT8q6++Os1iv/32S7e1EQugd9xxR825sbIT35+4TTQqfxwv23Zi4bBVhbe4ygJCfmnlU1PuGYoEKf+DqwItIqAV1z/4wQ/WTUKuB+LQTDCP47ayrXpo6pr+nnvuOf973nnn+UulEC3LF1UrZSEOBCAAAQiMLgLIoM41kiPKvFuxjDPaZVDJDj/5yU/c7rvv7hFIVglK0OxMmG7Ln7Fys979iONoWnq2jPF+vDhqXnqtKDXDdWGwO+zn/cYK0FguzYtb5FicVicy7miTW1tlgHzbKinidZtA/ZVRup0z6UNgDBLQIjS//vWv05J/+tOfTrdb2ZAvvxDkN/Pyyy8Pu5X9xgKeFCytCBJnnnlmWl5NCY6nMKUnSthYb7310lR+//vf14wYpyeijZ///OdO0/OrDrLw/fznP58WQ0LPNttsk+5rIxZAtciO2kOzIAX6mmuu6YXaww47rFn0rp8vu+3EwqF4xH6q8iojh/I//elP3RFHHOH23HPPtkbp41VeNVLdSPmpMsRuDbTfTns7+uij/T2U24Ngfa20skHK3FNPPTW1HJa/Jy3ORIAABCAAgf4jgAz65j1HBs1v+5I7P/rRj6Ynb7/9di8jpQfe2ui2/HnppZdmsxy2/9vf/jY9tummm6bbYSNe/LFZv+TGG28MlzX9vfjii5vGkawdggxdygplybhVyK2tMkC+bZUU8aokgAK0SvrkPeYIXHLJJX6xFBVcvv922mmnQnXYfvvt/eJB4SIp7KoOG220kQurMWoVxiOPPLJhkaSYii0QNMLcrSDlqsqnoBHg7373u3WzkvP3ZmWve3GJJx588EHvkP62225LU5WVcDyarRPvfve7naa+K8gfkxR4jYIsDaX0vPXWW518yMbO1Btd181zZbcdWQEEpbcUi1JsNgrxYMSGG27YlgP92GJ4wQUXbJSdd4SvVVbjkLUAnW222dLT9XxNadEH3UMpM+X7s1FQ24in2ddb9KpRGpyDAAQgAIGxTwAZ9M1FN5FB67dlzS6L/WkeeuihTnJpHLotf/7hD39wt9xyS5xlzbZ8n4eZLTqRNRDQsXjRISktYzlI50PQTBkNhLca5F6o0eC6ZGw9ZwqaFdRMNm81X8UrS8YtW24tUodmcZFvmxHi/GgggAJ0NNwFyjBmCMSO52Ut1uqKlaGCWjl9u+22C7v+IztSvg7TTDMbUtjEwsNxxx3np+dnovldCVFatToofRZddFFX1Ao2L91Gxw4//PD09Omnn+4++9nPpn4Rw4m77rrLrbXWWpUoBaXk0gqRV155pdN07JVWWslddtlloWj+fseWv+GEpn9r1fGwiI6uOfjgg1O2IZ5+Jfip3mGBHF2z9957x1Eq2e5G29GCP8FXqqw84mcurqQUzN/+9rfTQ5/5zGfS7SIb8vMZgqa/P/HEE2G35ldCthS+4R6Ekxo0iEPs5F7KS1lgZEP8DpCFpwTyekFK/yD4a7EsPXMECEAAAhDoPwLx9xAZ1PnvYb/LoNmnQP2S2FhArqG0MnwcRkL+lNwbuyoI+Wvq+w477OAH/nVM/tbzZt7IN3sI119/vTvppJPCbvqrxVg333xzp1lUrQYZU2ywwQa5ZZNidOedd06T2mOPPfxiUumBEjbKkHHLlltLqFaaBPJtioKN0UzAOlYECECgBQLmNyYxK77Enmf/d8opp7Rw1fAotuJzmobSMiVOTSTzY5ieP/HEE2vOZXdM2ZbGNQVc9nS6bx/8NJ7yNIfh6bmwYQJBTZwtttgiMYVjYgqcxKaz+HLa6tdpHBOgEvNxGS6v+S1SB10Yp2ujhzVpaeeQQw5J81X5zQouMYu/ZNddd01sJDuxUVp/ftKkScnEiRPTuDYtZlharR4wpVSajvJUutm/kK/O5/2ZxW9iyuKGWZrirubaNdZYIznhhBMSE/gSG4lOzNIhMWGnJo5ZRuamWYT7aG07qtjnPve5mvqaQJWYtWdiflUTWyAoMd+5iTmlT+MofhxMoE7PWYcxPjVs26wwE3PhkMa36U6JWWcmNjCRmJVtYgtZJfvss09ivot8HFM+JzaQkcY3xfWwNM1yIT2vtrrJJpskJswneocomHWr3w9tRs/SLrvsklx44YXJnXfemZjlRGLTw3wbD3HU1myl12F5cQACEIAABHqfwEjIoFXJELp7ZcmgReqgfJvJn4oz0jJotp9gFn8qRstBcqdkySA/6FeyZDZ0S/4M+drq6ckPfvCD5Nprr/V/kl1tzYC0XHPNNVdihhXZYvl9c/eQLLLIImlcyXyS+4855hgvD0pmCrKWfiW7hXxtYLomzc022yw9Z9axfnuxxRZLDjrooER9J/VlbHaP71uENFR2m3pfk07YWX/99dP0bLp8OJz+SmYN6ai82dCpjNsNubWs5wb5Nnu32R+NBGRZQoAABFogoA93+KBJYTJ16tQWrsqPEn+o9YHXhz6EIh+hMpVYUoqa/6C0jqGueb8SYKSIqheK1EFptCKA2irpifkEqls+KQlten5NWra4Tb0iNj2eVYDmcah3TMqyH/3oR02VnyqEWZAmZvlZo1yvl66O23T6umUvwn20th1Vzkb1k6997WuJlI2NWOicBGLFj0MRBaiuk4DaLB+dn2+++RLzRZt89atfTeNLGZsN3/jGN9LzcbpqUyHYavKJWXTmxouvCflKQCdAAAIQgEB/EhgJGbQqGUJ3tCwZtEgdlG8r8qfijaQM2qkCVOW1xTVT4wDJEeZTPLHZSjqVhm7JnzJOiJWXWZlG+1JAasC3UZC8pUHkvOvDMVvlPrEFqmoGlRspQG16e2L+6xumae6pErMGrVu0ThWgncq4KljZcmuZzw3ybd2mw4lRQoAp8PYGJUCgFQI2eppGs9FE14kvvnhKtHy5tOKUO828Sxs2EusXXJHvnlVWWWWYz0plawogP81bK5zHjta7VKSaZHfccUfvU0g+GM061U99MgHLbbXVVs6UjX5xGhOEnBYJCMEE27DZtV8tVCNemsaj1TflO0irhZpiLJdhtiBmVep9XWpxnXXWWafuglKaDqQ4Rx11VDaJyvfLbjtaVEurmcqtgKb6ZP2nqsKLL764k6N6LdzV6SJcciegdm9K4VyWysuUmn6alVlz1rR9lUFT3eMgtw2mwHVqn3GQb6QQ1DY1jV/ToepNazerBt/W5UvLBO5wKb8QgAAEINBnBJBBkUGLNHn5+ZQcEoLc+NhslrDrf7slf2qxTvnS/NjHPuaURxwkL+67777+/KqrrhqfGrYtecsUuX7KulmA1py3GV/e374pi92yyy5bc67RjlwE6Br1YbJlU5p77bWXl7XjBYsapdfOuTJk3LLl1nbqUe8a5Nt6ZDg+WggMSBE7WgpDOSAAgdFDQL515KhcC7XIobqcd2vV6qzAMHpK/OZCSVo5MrzWXnrppRpn8KOprPXKosV/5Ifo7rvv9v5AJdhJsWvTcepdMuqOl912lJ5Z8/o/sz528oUppWTwn1oWACky5bNKC31pYELs1eY7GeyQT1H9qbwqe16QT91HH33UyTeW/qRUX3311d3CCy+cF51jEIAABCAAgZ4mULYcMRKw5F9yLMugncifWsjIXAZ5zOa+y8l/poLu48033+xlmyBTSQlaNMjfvvz9yw+7Bsbf9a53dSwDqmwaYDb3Es5cBjizDC1arFLidyLjdkNuLaVSbyWCfFsmTdIqiwAK0LJIkg4EIFA6gSeffNIvKLT88st7R+TNlIA2ncavrq6CaEVvXU+AAAQgAAEIQAACEIBAEQLIoK3TqqcAbT0FYkIAAhAYGQLjRyYbcoEABCBQnICmvJgvIX+hrFC1Cv0CCyyQm5CsPs03Y3rOfEOm22xAAAIQgAAEIAABCECgVQLIoK2SIh4EIACBsUMAH6Bj515RUgj0HQFZfIZpwC+//LI78sgj6zI4+eST3TXXXJOe33LLLdNtNiAAAQhAAAIQgAAEINAqAWTQVkkRDwIQgMDYIYAF6Ni5V5QUAn1JQE7bDzroIF93LRjz0EMPOS2IJEfmsvqUz8TzzjvPnX/++SmfzTff3G277bbpPhsQgAAEIAABCEAAAhAoQgAZtAgt4kIAAhAY/QTwATr67xElhEDfE9hhhx3cb37zm5Y4bLrppu7Xv/51RwvXtJQRkSAAAQhAAAIQgAAEepoAMmjz24sP0OaMiAEBCIwOAkyBHx33gVJAAAINCJx99tl+dck111yzbqy1117bnX766e7iiy9G+VmXEicgAAEIQAACEIAABFolgAzaGqmBgYGOV2ZvLSdiQQACEGifABag7bPjSghAoAICTzzxhJs8ebL/0xT4RRZZxC2zzDJuiSWWqKA0ZAkBCEAAAhCAAAQg0A8EkEH74S5TRwhAoJcJoADt5btL3SAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0OcEmALf5w2A6kMAAhCAAAQgAAEIQAACEIAABCAAAQhAoJcJoADt5btL3SAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0OcEUID2eQOg+hCAAAQgAAEIQAACEIAABCAAAQhAAAIQ6GUCKEB7+e5SNwhAAAIQgAAEIAABCEAAAhCAAAQgAAEI9DkBFKB93gCoPgQgAAEIQAACEIAABCAAAQhAAAIQgAAEepkACtBevrvUDQIQgAAEIAABCEAAAhCAAAQgAAEIQAACfU4ABWifNwCqDwEIQAACEIAABCAAAQhAAAIQgAAEIACBXiaAArSX7y51gwAEIAABCEAAAhCAAAQgAAEIQAACEIBAnxNAAdrnDYDqQwACEIAABCAAAQhAAAIQgAAEIAABCECglwmgAO3lu0vdIAABCEAAAhCAAAQgAAEIQAACEIAABCDQ5wRQgPZ5A6D6EIAABCAAAQhAAAIQgAAEIAABCEAAAhDoZQIoQHv57lI3CEAAAhCAAAQgAAEIQAACEIAABCAAAQj0OYHxfV5/qg+BQgSmTp1aKH6jyBMnTnQDAwMuSRL3xhtvNIo6Js5NmDDB12XGjBljoryNCsm9aUSn2nMvvPCCL8Dg4KCba665qi1MCbnz3JQAsUtJcG+ag500aVLzSMSAAAQ6JvDiiy+6suSr8ePHu3HjxvkySf6UHDqWg+QBva9ff/31sVwNX3buzei9hVOmTHGvvvqqL+B8883nZp111tFb2BZKxnPTAqSKonBvmoPvRP5EAdqcLzEgkBJ47bXX0u1ON+aee26nF9zMmTPdtGnTOk2u8utnn312L0S/9NJLlZel0wL04r0ZGhpyvXBvHnvsMae6SEm95JJLdnqrK79ez02v3Jt55pnHD+r00jut1+6NFCi98L2p/MGlABAYYQJS7k2fPr2UXCXj6Nuj8Morr5SWbimFayMR1UV/YYC0jSRGzSXcm1FzK4YVRArQoGSfZZZZhp0fawd4bkbvHePedPfeMAW+u3xJHQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKiQAArQCuGTNQQgAAEIQAACEIAABCAAAQhAAAIQgAAEINBdAihAu8uX1CEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEKCaAArRA+WUMAAhCAAAQgAAEIQAACEIAABCAAAQhAAALdJYACtLt8SR0CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCokAAK0ArhkzUEIAABCEAAAhCAAAQgAAEIQAACEIAABCDQXQIoQLvLl9QhAAEIQAACEIAABCAAAQhAAAIQgAAEIACBCgmgAK0QPllDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAC3SWAArS7fEkdAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQqJAACtAK4ZM1BCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0F0CKEC7y5fUIQABCEAAAhCAAAQgAAEIQAACEIAABCAAgQoJoACtED5ZQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAt0lgAK0u3xJHQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKiQAArQCuGTNQQgAAEIQAACEIAABCAAAQhAAAIQgAAEINBdAihAu8uX1CEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEKCaAArRA+WUMAAhCAAAQgAAEIQAACEIAABCAAAQhAAALdJYACtLt8SR0CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCokAAK0ArhkzUEIAABCEAAAhCAAAQgAAEIQAACEIAABCDQXQIoQLvLl9QhAAEIQAACEIAABCAAAQhAAAIQgAAEIACBCgmgAK0QPllDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAC3SWAArS7fEkdAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQqJAACtAK4ZM1BCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0F0CKEC7y5fUIQABCEAAAhCAAAQgAAEIQAACEIAABCAAgQoJoACtED5ZQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAt0lgAK0u3xJHQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKiQAArQCuGTNQQgAAEIQAACEIAABCAAAQhAAAIQgAAEINBdAihAu8uX1CEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEKCaAArRA+WUMAAhCAAAQgAAEIQAACEIAABCAAAQhAAALdJYACtLt8SR0CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCokAAK0ArhkzUEIAABCEAAAhCAAAQgAAEIQAACEIAABCDQXQIoQLvLl9QhAAEIQAACEIAABCAAAQhAAAIQgAAEIACBCgmgAK0QPllDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAC3SWAArS7fEkdAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQqJAACtAK4ZM1BCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0F0CKEC7y5fUIQABCEAAAhCAAAQgAAEIQAACEIAABCAAgQoJoACtED5ZQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAt0lgAK0u3xJHQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKiQAArQCuGTNQQgAAEIQAACEIAABCAAAQhAAAIQgAAEINBdAihAu8uX1CEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEKCaAArRA+WUMAAhCAAAQgAAEIQAACEIAABCAAAQhAAALdJYACtLt8SR0CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCokAAK0ArhkzUEIAABCEAAAhCAAAQgAAEIQAACEIAABCDQXQIoQLvLl9QhAAEIQAACEIAABCAAAQhAAAIQgAAEIACBCgmgAK0QPllDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAC3SWAArS7fEkdAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQqJAACtAK4ZM1BCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0F0CKEC7y5fUIQABCEAAAhCAAAQgAAEIQAACEIAABCAAgQoJoACtED5ZQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAt0lgAK0u3xJHQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKiQAArQCuGTNQQgAAEIQAACEIAABCAAAQhAAAIQgAAEINBdAihAu8uX1CEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEKCaAArRA+WUMAAhCAAAQgAAEIQAACEIAABCAAAQhAAALdJYACtLt8SR0CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCokAAK0ArhkzUEIAABCEAAAhCAAAQgAAEIQAACEIAABCDQXQIoQLvLl9QhAAEIQAACEIAABCAAAQhAAAIQgAAEIACBCgmgAK0QPllDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAC3SWAArS7fEkdAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQqJAACtAK4ZM1BCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0F0CKEC7y5fUIQABCEAAAhCAAAQgAAEIQAACEIAABCAAgQoJoACtED5ZQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAt0lgAK0u3xJHQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKiQAArQCuGTNQQgAAEIQAACEIAABCAAAQhAAAIQgAAEINBdAihAu8uX1CEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEKCaAArRA+WUMAAhCAAAQgAAEIQAACEIAABCAAAQhAAALdJYACtLt8SR0CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCokAAK0ArhkzUEIAABCEAAAhCAAAQgAAEIQAACEIAABCDQXQIoQLvLl9QhAAEIQAACEIAABCAAAQhAAAIQgAAEIACBCgmgAK0QPllDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAC3SWAArS7fEkdAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQqJAACtAK4ZM1BCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0F0CKEC7y5fUIQABCEAAAhCAAAQgAAEIQAACEIAABCAAgQoJoACtED5ZQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAt0lgAK0u3xJHQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKiQAArQCuGTNQQgAAEIQAACEIAABCAAAQhAAAIQgAAEINBdAihAu8uX1CEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEKCaAArRA+WUMAAhCAAAQgAAEIQAACEIAABCAAAQhAAALdJYACtLt8SR0CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCokMD4CvMmawhAAAIQgMCYIpAMOTd036AbumucS54ecMlLA25gDucG5h9yg6vOdIPvHHIDfFnH1D2lsBCAAAQgAAEIQCAQmHGbyXnXjHdDT5it2GsDzo1z7m0LL+JeXnGqe3XZaSEavxCAwBgkQDdtDN40igwBCEAAAiNPYObt49yM8ye45KnayROJL8o4N/PKCW5g3iE3fuvpbtzaM0e+gOQIAQhAAAIQgAAEINAWgRm3DLoZv57FuVdM6ZkJs97/Nqe/oYkz3dByb7ih3RM3OGcmErsQgMCoJ1Dbixv1xaWAEIAABCAAgZElkJguc/rZE9z0E2cZpvzMliSZMuimnzqLm37GRJfMyJ5lHwIQgAAEIAABCEBgtBGY/qsJbsbJ+crPuKyDb4xz4/85m3vj67O5ocnDFaVxXLYhAIHRRwAF6Oi7J5QIAhCAAARGEYEZZ050M6+aUKhEM68f76afPrHQNUSGAAQgAAEIQAACEBhZAtPPmuBm/n/27gOwrepc4PgneTuOs/cggyQ0EEIYYYc9ChQoZZU8RgmFAGU9oBRogfIgrLJH2RvKKFAoG0ICGQTIgEDIJns5Tuwk3tZ457vOdWRbsjWubNn3f1oj6Y5zz/kdWbn+dMYUvc+LIaBZ5ZGqO7IlUNi8ZeVqCCCQmAAB0MT8OBsBBBBAoA0L+KeZoe1T4pstJvBduvgmxnduGyalaggggAACCCCAQEoI+H/0in9SnPdq/pogaEpUhEIggEBUAgRAo2LiIAQQQAABtwkEK83Q97dj6/lZ38j3npkztKz+Vl4jgAACCCCAAAIItLRA9fM6WieGnp/1C7zFzBs60aySREIAgVYhQAC0VTQThUQAAQQQaG6BwBxzQ2tubBNKpR7xfxNnz4KELszJCCCAAAIIIIAAApEErDk8tyQQ/Nyese/jxL4sj1Q+tiOAgPMCCf5l53yByBEBBBBAAIFUEPB/78w3+oEfnMknFUwoAwIIIIAAAggg0BYEfJ/GOO9npEoXeSRgRg2REEAg9QXolpL6bUQJEUAAgTYvoCutV91rVt9sInUtHyhBc4zX45HK7KaPbyK7RncHlzvzHWFggVcq7w5f1sL0Sqs+Pl/4/Y0WMIGdmX+qFE9uAhlwKgIIIIAAAggg0IhAoERk+QVbJBjMaeSoFtxV4dS1zVygV5k6puj33d7Bfsm8ssqpypIPAq1agABoq24+Co8AAgi0EQET1QwuavrOMUvyaiusgdBWkXyeiHWrssKfWoum6+5oXU3AmYQAAggggAACCCRNwMTcauZBT3yYedLK6FTGZlX4VE3BAme+0E/V+lEuBGIR4LchFq1WdGxZGatutKLmoqgIIIAAAggggAACCCCAAAIIIIAAAkkSoAdokmCbO9vi4mL5+OOP5bPPPpOVK1dKRUWFZGdnS58+feTII4+UU045RXJzW994R7/fLxs2bJDevXs3NynXQwCBZhTwmH+Nsu5t+oub5ctXSCAQkMzMDOnbt29SS1j1zywJLkm8Z6anrxl6dFX4yaE6duxk1Wfr1i1Jq0vQdJWdW+KVr4rTZXWFV4qqPdJxabb0yw3ImG4+2b1jwEwpkLTLkzECCCCAAAIIuFGgo0jvh9tJYeHGpNb+e3OP8+qmTFlcmSZbzQiXdHNP09EblAPyfPI/3aukS4SIR/WLmRJcFmFnTCUOSvpV5eLNj+mk5ju4XfNdiishkOoCTvzGp3od23z5pk+fLrfddpts27bNqqsGPvv162f+sSmUpUuXWj+vv/663H///bLzzju3Go8FCxbInXfeKUcccYScffbZrabcFBQBBOIT8HRo+ryAuZnVAGgg0yvRHN90jpGPSBvpF58DAVDvyEDEsqZ18ognYH6SFICcsjFN7l+UJUtK6gVyt9bU++llWTKonV+uHFoph3ZnXHzkdwN7EEAAAQQQQCAWAa8Za5o1KF28SZoC9LtNXrnhxxzZUFlvUKv54ndDQGRhcZo8V5wp+3Xxyz92L5f8zLqlTzvE3Oc5EQA107in71o3b14hgEBqCtT7tEjNQlKqyAJffPGFXHfddVbw86CDDpJnn33W6gX66quvyqeffiqPPvqoDBs2TLSH6FVXXSVr1qyJnFmK7fnoo4+s4G2KFYviIICASwS8+5iAYFqCM416gpI22tfsYgFT7H8szJJLZ+c2DH7WK80vpWly+ZxcuXN+lvgTrG69rHmJAAIIIIAAAgg4LvDYkgwZNzO3YfCzwZU8MmNTuhz5ZZ7M21I39OE9wIH7PHM97+58gdyAnQ0IpKhA3U+BFC0kxQovUFBQIPfcc4+1c4899pDbb79dhgwZUufg3Xff3er52b9/fysIqgFSEgIIIIBA0wLeriZ4OSax4KV3X794+zR/VHGCCWa+uLxeV4cmqvzqykz5v3nNuxp9E0ViNwIIIIAAAgggUEfgyaUZ8vhSvV+JfvhMhRltc/Y3ubI6ZLYl7aGadkRi93lihtqnn8cK63UaiBcIpLAAAdAUbpymivbvf/9bSkpKJD8/X2666Sbx6qd4mNS+fXu54IILrD3Tpk2rHSofeqjP55OFCxfWziO6bNky0W3hku7THx2GWj9VVlZa+1atWlVn1/Lly625SXVj0ExIt2LFCtEenloeHaofmnTeT81/69aaMZqbN2+2XuujHqv7Nm6MPJfMpk2brGOKiopqs9V5UfU87QlLQgABBKIVSP9dtXh6N/ysi+Z8T9eAZJzZ/DfF/12bLm+sii34adfn7TWZ8s7qDPsljwgggAACCCCAQMoIaC/OR5bEFvy0C+8LemTsjLoTYmacXi3SLb77PPNXraSPrRIv3x3bxDwikPICzAGa8k0UvoAaRPz888+tnWPHjpVu3bqFP3D71jFjxsgTTzwhQ4cOlfT0us0+Y8YMeeihh6R+0FJ7jd54440yfPjwOnmfc8451usPP/xQNLgamjTQqcFWXbRI5x2104UXXig5OTny9NNPy9VXX20FI+19+qhzfI4bN07S0szk1SbwaV9D92mgV3/OOOMMaw5T7emqZXvllVd0d4N0ww03yM8//yz33nuvjB492tqvw/+1x+z48eNFvUgIIIBANAKebJGMyyql+n6zIFJB+C+ZwubT2QQ/r6gUT17YvUnbWGFGYT1o5vxMJD20OFOO6VktuXX/qUgkS85FAAEEEEAAAQQSFvjLXHNjFkPPz/oXLKr2yjO/mOHzg0zgc3vK/FuFVN1oJirdFn2PUg1+ph3tk3QzjygJAQRaj0AMf821nkq5oaTr1q2r7QU5aNCgJqusgUUNZNYPfs6ZM0euvfZaK/h5+OGHy1//+lcr6HnwwQdbPTYvvvhimTlzZpP5R3NAeXm5aH6lpaVWsFMDoYcddph16ksvvSQaUNWkq9VffvnlosP3Ne2///7Way3ToYceagVStUen9litn3S7Bj+7du0qe++9d/3dvEYAAQRiFvB2C0rmjRXi3Tt8r/j6GXpH+iTrr+b4Xs0/9P2zDelSUH8xgPoFbOL1piqvfLKeXqBNMLEbAQQQQAABBJpRYI0Zvr6iLPHwRf0pgry5Ipn3mFXch+l9XtP3boEsv6T/oUqs3qPNWH8uhQACiQvQvyNxwxbJIXTY+MCBA+MqQ1lZmUyYMME699JLL5UzzzyzNp9jjz3WWlDpueees3pSvvDCC5KZGd+QSjtTDYBqYFLnIdWV6jWdfPLJct9998k777wj77//vvzmN7+RrKwsOe2002T16tUyd+5cGTFihPXazkcDtR988IF88skn1gJP9nZ91G2ajjnmmDpTAug1qqurpUuXLtZ+/oMAAgjEIuAxI6Yyx1dJ4Bef+Keki3+uuQEPnUw/3/QEGOGXtIN84h0S71CqWEoU/tjJBc78sz65IE1+23dH74jwV2MrAggggAACCCDQPAIvLI9v6Hv90hVVe6TIzFDUKeRPW6+5fcq8Vu/zPFL9eqYEV5j7POt775peocG0gFQM2CZlw4sl/5BsSe9YdxRk/WvwGgEEUlPAmb+UUrNubbpU9vyYHo/HCirGU9lvvvlG1q9fbw0n14Bj/aTD0D/++GMrEKlzddq9NesfF8trzdMOftrn6fB8DYBqr9Zo0nHHHWcFQCdOnCgauNXerZp0WgA7AKrHhKaddtop9CXPEUAAgbgEvIMC4h1kvvU3ZwcrzX9KzY1xu6A8YhYQmr/VfBbpz6zos87M8Jm+BkHzBY0ZeuVAmlVU83mYaFZfmxVTL5kVW5mysiqsQWn6WVxZFdu5kco7tL1frhza/POoRioP2xFAAAEEEGhrAgUVIqdNrzs3phN11L9TPZ4Ss26EM3lvNYFLZ5JHjvuqnWRG6ky6r7mK+UnzB6Wr6XVaYW76MnKrpL1X77G6yNo5uVIZdOZ+q7H69MsNyMv7lTd2CPsQQCBGAQKgMYKlyuG68JEm/UNzw4YN1pybsZZN5+vUpEPF7SCitWH7f3S4/KhRo6zApC5a5ETSuTvrp+7du1ubIi26VP94HRrfr18/a9i+Ds/fd1/9V0rk+++/tyx0qH+469TPh9cIIIBAIgIe7YiQVTNU6sctaTLDBA1jT/ZQq3jOjf1q0Z6hq6VOLYy1TKHzYMV6bviS6ZymJAQQQAABBBBInkC5+bdW58ZMXkpm3vGVutTvldIo7jEKawYtilQ6E8SNpbS+0liO5lgEEIhGIPU+jaIpNcfU6fWpq5vHk+ygZq9evSKerosZadLh6E6kcEPQMzJq5prTYG606de//rV16Kefflp7ivZW1WTvq93BEwQQQAABBBBAAAEEEEAAAQQQQAAB1wo400XDtXwtV3ENWup8mjoXqPbkPPDAA5sszJ///Gfp2bOnnHjiidZq6vaCSI31vKys1DGeZgjA9mHmTV1E59lsLHm9zsTcdY7Sp556SqZMmSI6t6jmO3nyZGue0iOOOKKxIrAPAQQQcFxgwogKCe2tuLF6i3xbskhWVBZIkb9Usj0Z0jUjX/bIHSS75e4kaZ6az8IOHTuaoWEB2bZ1qyNluuaHHPlZh+EnmIaZoef37xHbsKuu3bqZoW7V2j37AABAAElEQVQeCfj9smnTpgRLUHN6VuJVcaQcZIIAAggggEBbFKgK+OSrktkytM9i2RLYJtXBasn15kivjG5yZPt9ZOfsvnFXO8us+dCuXTvZ7NA9wcOLs+T7Lc6EL/72q3IZ0C76OdvLCteK17/97+IOPSQj26yclOTUnvUokyxM9m4UcOYTxI1yLVxn/SPzoIMOkv/85z8yffp0GTt2bKMlWrBggXz99dfWMRoA1dS3b80/aI3NvalzhGrq1KmT9aj/0WCj/sEeLnC6du3a2uOS+aSb+UN79OjRovOY6o+WSRd10gWS2rdnUupk2pM3Agg0FOi6fSj8yorN8vC6L2Ri8YKGB5ktb22eLt3S82R8r0PkpC57SNc8EzA0w82LfdH3gA+b8faNh3bzORIAPcTk0zc3tjL1bO+1AqB+M2dWTnls5zZWJ/YhgAACCCCAgLMC+rfchFUfyX82fy+Beiufbw1skfW+9TKn/Efpkt5O/tb/BBnTYUjMBcg1McIOHdJlnbmCE+nc6ir5/ofEwxcZnqCc1t9a4SjqYq0srZLKypo5yXvmV5u/N52pU9QF4EAEEHBEwJnueI4UhUxiFTj11FOtnpm6Uro9/DtcHtor84EHHrB26fyYO++8s/V80KBB1qMucGT39Aw9v6SkRGbMmGFt2mOPPWp35eXlWc+Liopqt9lPfvnlF/tpwo92b1H9Bzpcshc6+uqrr0TroInh7+Gk2IYAAs0hoEHPMxc+FTH4aZdho69E/m/VB3LFL6/JNp9ZecDBdEyvarMQUaLBx6Ac2yu2PwwcrAJZIYAAAggggEASBTZXl8ix8x6StzfPaRD8rH/ZTWYiyit/eV3uNMHSlk5H9PSbhYsSvccR2bNTFJN/tnRluT4CCCRFgABoUlibJ1Nd2fyMM86wLjZhwgR57rnnZM2aNXUurj0yr7/+epk3b541PPzyyy+v3a89SIcMGWINVXz00UfFb4Yt2kl7dz700EOybds2GTBggLVQkr3PXmBIrxea5syZI2+88UbopoSeZ2XpCiNirVQfLiMtv/b21J6t2gtW5xfdZ599wh0qU6dOlc8++8yaLiDsAWxEAAEEEhD4vHi+XLvs31IRaHwakNBLTNu6VM6d84RUmuFnTqWBZkX6E/sklt/xJvi5c174L56cKif5IIAAAggggEDzC1SYe46T5/9TCs2XsbGkNwpnye0rP4zllKQc+4cBNcPQ481cvyS+Y0RsU/zEey3OQwCB1BNIvA956tXJVSW66KKLRHtqvvfee/Lss89aP7pw0bBhw6zV25csWWINVdc5PG+++WbZdddda320h+XVV18tV111lbzzzjtWkFRXVNcel9rzc+nSpdZq6g8++KDY84Xqyb/97W/lp59+subcPPfcc0V7h+qxuu2AAw6wgpG1F0ngib0A0wcffCDz58+3hvxfcMEFtTlmZmbKkUceaZVdN5511lkR5yq9//77paCgQMaPH28FdGsz4QkCCCCQoIAOe79p+btx5TJ76wq5bfG7cnUP5+YuvmZohczanCary2P/jrN3dkCu3SWxPy7iguAkBBBAAAEEEEi6wLhFz0vJ9rksY73YW5tmy/75g+TwjrvEeqpjx186pFo+Wp8pK8tiv8cRE/y8eHCldLVXdnesVGSEAAKtRSCeT47WUjdXlFODmNdee6383//9n4wcOdKqs/b6nDRpkui8n7qyugYJX3nlFRkzZkwDEw2Ivvjii9Z8mhrEfOmll6xjt5oFOY455hjR4Gfnzp3rnHf00UdbvUrz8/NFh7y//fbbsmjRIjnppJOsctQ5OIEXOsT9sMMOs4KaWrbZs2c3yO3444+v3cbw91oKniCAQDMKPLR2olQE4+91+eraGbKkvMCxEnfIFHl0z3LpYYKZsaTuWQF5ZK9y6ZyZ+PCyWK7LsQgggAACCCCQfIFvti2T+eU16zvEe7XbVn4Q76mOnffG/qXSJTO2exwNfuoIl/E7Rz9Sx7ECkxECCKSMAD1AU6YpEivIoYceKvqjCwFt2LBBiouLrVXidbX40N6b4a6iK8Pfe++9UlVVJStWrJCcnJzaBZLCHa/bNDipPzrkXq+p84naK8Xrauz106efflp/U+1rLaOu5l4/ablvvfVW0TlMdUVhHeJeP9l107lNdah+pPTWW29F2sV2BBBAIG6BdVVb5IstC+M+X08Mmv/9a+N3ZpGBHV/oJJShOXmgGcL+2n5l8refsmVqYdP/1O/fxSe3mZXsu21fzCnR63M+AggggAACCKSWwP1rPk+4QMX+cpmyZbEcHMeiSAlffHsGuea25rNDSuWCmTkyuyjNbPU0mnWauc+6bGilnD+Q4GejUOxEwAUCTf9V5AKEtlTFXLPc3sCBA+Oqkg4p1zlBY0l9+vSJ5fC4js3IyBAN0oZL77//vrX5hBNOCLebbQgggEBSBSZvWeRI/l+ZfILB46xV1B3J0GTSxQQzHzM9Or/ZlCbvrMmQKRvTzaJLO/5IyEsPykFdfXJK32rZr8uOOaCduj75IIAAAggggEDqCCx1aLTJ6xtntmgAVEXTzTjW50eXyw9FXrlzQbYs3OYVX3DHPY5+vZxnIh0Hmi94b96twnqeOi1BSRBAoKUEPGaINGPdWkqf68YlsHr1atGg6MyZM62eqxr01R6e9qJJcWW6/aQvv/zSyitcHjoVwHXXXRduV1zbsrOzrWCH/gpWVDi7EnRcBUrwJA2ga9KexK090Tap24KzZs2y5inW95tO+7GmokgmLPlvixX4x22rZbWZA9SJdHiX4ZLlTd73kvqvfZU/y/rJTKsU/fFs/1thzw47ybh+hyRUDfv3RueRrqxs/fOItsXPNKfbRkeMkBBAIHEBXXT0xhtvjJjRxRdf3OhIp4gnhtmh99D2CCr9rNbPhdactC5ap/Lyll3Y5saFb8qkwgWJUZp/kz3bezPq6BD9v1PJFwxIYfU2R7LzmjJ2z8xvOq/t9WmucIPPnyk+fwfxeqrlqZG/k1GdejddxiiPmDt3bu29jY467NatW5RnpuZhqfJ744RO6Gea/k3dXO83J8oeLg/aJpxK3W2J3H8m7y+tumXkFQKOCTz22GO1Q+Y95q93XeXeieCnFnDlypXyySefhC2rLsp0yy23hN2XyEatQyK/xIlcOxnntqW60DbJeIc4l6e+16qrN8tHG+c6l2kL5vTFpp9b7OrpaemOfQ7p3NRt6XOgLdWlrbVNi/3CcGEEHBbQQGSk+0+91Nlnn52Uz1Wn7p8d5ogru5b+rJ5Xsk7Wm2lx3JACJjKbunXdaAWO/ZnVjv7O6N8EdtKAW0u/3+yyJPrYVuphO+iX8W0l0TbJaUkCoMlxJdckCuy5556yePFi0eH3OvT9wAMPTOLVyBoBBBBAAAEEEEAAAQQQQAABBBBAoDULMAS+NbceZXdcQBePKigIvxqzftuXl5fn2DV1USftjeP3+2XzZmeG0DpWuDgy6tChgzXkYOvWrXGcnVqndO3a1ZqeoC21jQ5x0yF2rT0tXLiwdgj84MGDpSrgkxUVm1qkWgGzEMC7i1+Q1Zu+kfb+MtMfwiPb0nJlVVZPWZHdW/wenZg/uqTDyV7+1QVJHQLfWEnap2VLz6wOjR3S5D5+b5okarEDktU2rX0IYIs1CBdGoJ6A3m8sXbq03tYdL/X+U+9DnUial92zqKioSHw+nxPZtlge2uOrffv2snGj6fnXgmlR2XpZleCUONnZOWZUW82UUiUlpeZvBOfaxhcIyrXL3nREqGdGB7mu/zFN5pVhpivKNaN1tmxp/p6x++cPltz0rCbLqAdUVW2VhYuflA0FU6SistDczwUkO6urdO92oOwy9CLJyuwkv/zyS+0QeB0VqH/3tOaUKr83Thjq77/d85PPNCdEncsjWW2TyP0nPUCda19yagMCHTt2FP2JlNatWxdpV0LbW/vNp1Ze51vRn7ZSF3uoS1upT1tpm9BfNG0bMwe+DMzsEro56c+rq0tk4ZLHZfHSp6W/CYL2D3PFCk+mTO8wUr5rv5sJhDb9T+3Idn1ll+weYXJqvk1OvtedzKv5BOpeqS19ptk1a4ufA3bdeESgNQukpaXJ0KFDI1ahsLDQTPnizArW+jlgJw28tvbPa3sO05aux6DMrqI/iSQNqunaBpqcbHO7TB3STDDS3Lckmo7ttKsckh/5/Wrnr3XROq0LJufvJ/s6kR6bek9UVRXLjJl/koKNUxpkUVq6UjZtni3zFz4sXbuMln69bzHH1ASn9T3XVN4NMkyxDanye+MEi10XzYvPNCdEncsjFdtG/3Z0PGlF28JCKI7DkCECCCCAQKsW2LptiXz+5XGyYNHD5iYr8h8R2cEqObz4Ozlv/XuS7ytpss76xwQJAQQQQAABBBBIlsBhHYY5kvXZPfZzJJ+WzGTjpm/l/U/2CRv8rF+uQnPsj/PPNr1DV9XfxWsEEGhlAk13S4myQrp4zOuvv27NzahdxPVbkX79+snOO+8sBxxwgBx11FFR5tT4Ye+9957oz5FHHilnnnlm7cGXXXaZtfrfXXfdJTq0uKmk5b311lule/fuMmHChKYOl/Xr18tf//rXiMfpN7f6LZd2x9U5Ko855hhrCG3EE1Jsx8cffyz//ve/ZcyYMXLOOedEVTqn2iKqi3EQAggg0MICJSXLZdKUU0wvnOiHcvWo3iTnbHhPnut5spSa4fHhUt/sznJyl1HhdrENAQQQQAABBBBwROAv/Y6VD4p+lOqgP+78xuQPkU7p4e9n4s60mU8sKvpJvpx6hrlqIOor+3ybZMnyK2TIwEfMOT2jPo8DEUAgtQQcCYBOmzbNCiaWlZVZtdN5ZXS8//Lly62fzz//XL7//nu58sorE56/RocD6BxwI0aMqCO5aNEi0etH2x29vLzcyqe0tLROPpFeaI9WvW406aOPPpLp06fLjTfe6Njq5NFcN5FjNm3aZNVv2LDovxl0qi0SKTfnIoAAAs0hEAhUybRvxsUU/LTLle8vld9t/Exe7HGimG/G7M3WY7rHK//41RmSIdHPF1onA14ggAACCCCAAAJRCGR60+WGvr+Wv696P4qjGx6S582SOwb+tuGOVrQlYOaOnzztNFPi6IOfdvX8/q2yfNXN0rfPG2ZTe3szjwgg0IoEEg6A6oTdf/nLX6wqjx07Vk4//XTp3Lmz9bqyslImTpwojz32mNVrU+euueGGG5LCc9JJJ1nD7u15U5Jyke2Zvvnmm1aAN/QaWjedfPurr76SV199VSZNmmRNMH799deHHuaK583ZFq4ApZIIINDiAkuXvSTbSpbEXY6+VQWya9lSmddu59o80s0Mpv8Y/nsZ3XGw6AJsJAQQQAABBBBAIJkCJ3XdQ5ZWbJSXN34T02WyzHzmLw47X3K8NfNgxnRyCh08+4cbzRRGNZ224ilWReUymbfwdjlw3wfjOZ1zEECghQUSDoBOnTrVqsIJJ5wg48ePr1OdrKwsOe6446xh5ldddZVMnjxZrrnmGsk0K8I5nS655BKns4yYX7t27UR/6iddPGfIkCHWEPx7773X6gWqE43bi6nUP76tvm7OtmirhtQLAQRSS2DRkicTLtC+W+fWBkB7Z3aUv+/0Gzmq5yhrVfuEMycDBBBAAAEEEEAgCoH/7XuUDMjuIneu+lh8UfSE7GPuWV4Yep50zsiLIvfUPmTl6rcTLuCGgo9MHgRAE4YkAwRaQCDhAKgObdc0aNCgiMXfe++9pVevXqIraM+dO1f0df2kw6mXLFkiBQUF0qdPHxk8eHCjq3HXP3/FihXWH5E672h6esNqLVu2TObPn2/NSap5JzMdcsghogFQ7dGjPWR1HtT6adu2bVZ9V61aJb1797YCp7pKXv20ZcsW2bx5sxVUzc/Pt3x++uknq7erBlvD1aWoqMi6th4fbj7UDRs2WNMF6HyleXnh/yHTOU/1OhkZGfKrX/3KCmLXL1uk1021hdZpzpw5UlFRYc0TO3z48IhBYu1FrEb6U1JSYr03+vfvL127dm1weZ1ywev1iu7XwLPO8/rzzz+LOujQ/nDnNMiEDQgggEA9gaLiH6W8Yn29rbG/7GnmA903s70c2v0AOaXLnpLhZdh77IqcgQACCCCAAAKJCpzSdU/5defd5LaVH8qULYulJFBZJ0udsKdPZie5qNcYOb5z3ann6hzYil5sLJxh4gVVCZc4YKxWrflA+vU5PuG8yAABBJpXoGGkMMbra2Bp5syZ8v7778spp5wiuhhQuPT8889Ldna2FaAK3a+BwKeeekrefffdOr1gNJClixyNGzcuqh6jF154oRXU04V8evToUXuJTz/9VJ588knRoJ+dBgwYIBdccIH90vHH1atX1+apvWBDk9/vl5deeknUQ5/bSeuriw+de+65dQK4Op/oo48+KpdffrkVBHznnXfsU6zH/fffX2677bY6RnrMc889J6eeeqpcccUVdY7XF/fdd1/tHKXHHntsnf2BQEAeeeQRa0Gr0B266NSf//xna1h/6PZwzyO1hQa4tawaFA5NQ4cOFZ0qoH6gWN9TDz/8sNWuocdrj9rf/va3oj1NQ331ujr/7NNPPy1XX321aNA7NJ199tnW+ynSezT0WJ4jgEDLCixf+aYUFE5vUIiSbSXWFxz6mblhU8Oe+A1OcGCDLn7kVDqz+GvJqVwuc1a9amWZlZklQfM/nWe6uVPvnkdL396/bu7Lcj0EEEAAAQQQSAEBHc5++4CTrZIU+cpkUfkGKaoulZ1M79Ah2T0k3dxrRZtmzfmLuW+bVudw/ZvNm+YVv2/H37x1DmjmF5VVRY5dcdacP8tPP9/pWH7JyKhL571k9F4PJCNr8kSg1QokHADVVcNfe+010ZXfNYCnganRo0dbvfBCVcLNzam99HRl9dmzZ1s9FU877TSrR6D25NN5NnUuTe3B99BDD0XsIRh6jfrP582bJ3fccYcVWNW8R44cKWvWrLGCe3//+9/rH+7Ia11USYObmrSHpfZmDU033XSTNU+oDpfXAK8GY7V3owZu9TwN2mmQsH7SoKn27NSgpprrokUvvviifP3111ZA056Htf55sb7WgKsGZo8//njZb7/9rOto++pCVmvXrpXHH388rrbQHqU6/YGW+7DDDpMDDzzQCmzq6vPaxrrvmWeeqe2xqgHcZ599VrRX7Iknnii777671atVF5f64Ycf5O2335aePXvK73//+zpV1MWtLr74YitAosFzddb3l87JqobaE/k3v/lNnXN4gQACqSewafNsWbmq8WFKm5y7j202gI2FXzfbtZq6UG5OHwKgTSGxHwEEEEAAARcI6Mru+7YfGHdNNxXNktKylXGf39pO9PlLxFdWktLF1i/YSQggUFcg4QCoDl++++67rUCmDjl+8MGa+TC0F+aoUaNkr732soJduip8/fTWW29ZwSkdAq6BtU6dOlmHaIDv17/+tWiPPh1i/+GHH1oBufrnN/ZaV4TXVdh1Vfibb75ZtAejnY466ig5//zz41504pNPPmnQE1KDkxog1N6wOtRfv/HSXojaS8lOM2bMsIKfukiUBvjsxaJ0v9ZXe6V++eWX8u2331pBZPs8fdT8tReoBnLtdMABB1j1+OCDD2SPPfaQ+r057eNiedTg52WXXWYtZmWfd8QRR1g9JzVQqfO4agAz1nTttddawc8//OEPVpnt8zW4qb1eddi89gLWdtFh7xrg1KSGodc744wzrICv9nLVdggXANWh7ho81R7Hmk4++eTac7RXKQFQi4X/IIAAAggggAACCCCAAAIIIIAAAq4QSDgAqkra41MDThrQ1FXQdR5PHXKuvfv0R+fk1OHHGvwKXRBIA16aNPBnBz+tDeY/2ntSg2EaXNUeiNojMZa0cOFCK+Cmc2SGBj81D81bV6vXofHxJDvIG+lcvaYG7kaMqDtfyssvv2ydovUKDX7qRu3pqIFZ7aX4+uuvNwiAapBYe9eGJu1Vq6533nmnTJs2zZEAqM6h+rvf/S70MlYvyrPOOkseeOAB0cBjaECyzoERXuh7QXv1ahuPHTu2zlE6HF2H6U+cOFH69u1r7dO5QdVI5/wMdy0NkGs5dPqEcEl7ItvBT3u/fY4Gp0kIIJD6ArsMuVgG9D+9QUG1F79O1aHzE2sv8OZIBRuny7wFd9deyhvwSq+t3aRrSWfJrcqxhrCXZZbLhvxCKWi/SYKeyN+4j9r9dunYYdfavDp0yDf1CUb8PKs9MAlPcnKaxy8JRSdLBBBAAAEEEEghgVEjbzf3MnWnH8vKyrQ6DRUXb4m9pKZTTqd56yRvdZGkl/ms83256VLSr5MUDe8lZt69mPNcXzBZ1q77MObzwp3Qo9uh0qf3ceF2pcy2vHY7pUxZKAgCqSLgSABUK6PBKw1k6Y/25ps1a5bVu1N7M+qwZO3xqEFJ7Y2pgTvtmalDvzVpADVcsrfrnJraMzGWuRt1vklNOnQ6XNK84w2AaoBQV7LXIfza0/Sbb76xAr4a4LvrrrusRYPCXVODgJq0V6guAlQ/2QsWqV/9pAtHhVvcyQ6yLlq0qP4pcb3ec889wzrr9AGawpWtqQstXrzYOkQXUwqds9M+b5999hH9sZMGg+sHezXYqT2MdaoFHQavKXQOVftcfdRFkOqn7t27W5v0fUdCAIHUF2jXrr/oT/1UXKQBw4D1Gdylc/Pc2OWZcsxbcI+k+b2y67qhstvaoZLlz6xfNOt1WUaFzO0zXxb0WCpBb91AaFpajgnqnmo+Y2t6p+sJ+mWYVZ+M4rD5sREBBBBAAAEEEEh1gW5dRov+hCb9m1//roupA4rpCJP92iuSvmC+eEIzs577pPPKddJv2jrx7fIrqTjTdKzZPuKvwaFhNvTudZRjAdBRu99qFhNunvvQMFVhEwIIxCngWAA09Po77bST6I8uiqTDmXWuyldeecUKXOmwZQ1u6XBxDWDpojX6wRguaU9NDXpq0Ep7EWovyGiTHQC1A1/1z9O84006t2TokH4tn87bqb0Yr7rqKmsRofoL+mgAT1c/16S9WhtL2oNWF8TQIKudIvV0shd8Uk8Nxoaba9XOI5rHpq6jK9JrQFvbLdpkB0DtskZznr43dDoAHbKuiybpde3UVCDcDiTbx+uj9hbTpEFrEgIIIBCLQFZWFxmQM0ZGfZ0tHcvzGz01tzpb9ls+SgYV9peJw6ZJReaOVVV10aHQ4GejGbETAQQQQAABBBBwkYB3xXLJfcqsN9FEhxUNjGaYAGn6bX+XsgvHS6B/dIHI7KzOkpPdS8orEhsRmJXVi+Cni96XVLVtCSQUANXeeFOnTjUfAHlWsDMcjfb4++Mf/2gFOzUIqov2aADU7s2ogS4NSoUOjbfz0X36o6mpoJd9jv1o9zSM1OPPvr59fCKPmtff/vY3a8i9zlmqCxLpyvb1h/Xb1xg/fnxt/e1t8T7qcHFNGiy169xUXtXV1REP0Z5I4ZJ9jgYSo72OnY+dZ6S2sI+zH/X9oAFlXXhJkwardSi8Ti2wyy67mF5h7ayFjuzj6z+Gzrtafx+vEUAAgVgFPJs3yZiv+0la+Y5gZlN5dC/pIsf/dLi8P2KiVGZUmX/j0mT4Llc1dRr7EUAAAQQQQAAB1wl416yW3McfFU8MnVU8vmrJ/ecjUnbZlRLo3Scqsz1HTpBp3/whqmMjHbTLkGsj7WI7AgikuEBCAVDtzq6BPg2+6QI8jfU+tIdq213gtTegBjW1p6P27gvXa097fdpJV/OOJQ0bNsw6XFcfD5cibQ93bDTbtC666JIu6KPl1tXeQ1ev1x6j2tNVe4HqcO+hQ4dGk23tMaEWtRvNE3v7kCFDaoPEdrDYDh6HHq/PtbdopKSLLYVLtpeuoh5rgLFPn5p/kLRna7ikvYQ//fRT0eN0CL7OI6vBTw106vymusBTaNKeoZrswGroPp4jgAACjgqYL+FyXnwupuCnff38yjw5dNF+8smuX8muv7pW2ucNtHfxiAACCCCAAAIIIKACpsdn7hOPxRT8tOE0YKqB05KbbhXTw8jeHPGxV8/DpXvXA6WgcFrEYxrb0a3L6dKz+9GNHcI+BBBIYYEdS5THUchdd93VCn5qEFMXQGos6Vygmvbdd1/rUYN0AwYMsJ7r0PFwyd4eae7IcOfY27SnoCZdeV0DbPWTztvpdNLh47pSuybtCfqf//ynziUGDqz549cO4NXZaV7897//tRYJ0t6P9ZP6hetBOXnyZOtQO+CrL7RHrqbQYePWBvMfHb5uBzPtbaGPkVy++OIL67BYA7d6kl3vn376KexCHz/88IM1LcBjjz1mXUPtNOkq9/WDn7rdnu80UoBXjyEhgAACTghkzJguaRG+SIsm/95be8jotN+LLupEQgABBBBAAAEEEKgrkPWft8Vj4gnxJj036923oz59zIGvSl67QVEfbx/YMf9Q6dX9Avsljwgg0AoFEgqAaq/M0047zaq2Lih0xx131PZItC10iPajjz4qb79d86F0yCGH2Luslb71ha58bs/Zae9csGCB/Otf/7JennrqqfbmqB81uLrbbrtZw9Iff/zxOnM/6kI+dt5RZxjlgbpa/V577WUdrdfduHFj7Zm6srkmvbYd5LN3alDy4Ycfthb60V6W9ZP2nNW5VEOTLir173//2wpCH3fcjlXo7EWAdHoCe/5NPU8DhroIVWOBQ12YSnv1hqZ58+ZZwVwNWp933nmhu6J6rsFZXXSqtLRU1CS056YGZO3rHXHEEVZ+dk9iDYzWD/rqFAo6lYImDbyTEEAAgWQKZH41OeHsd1kR/fzVCV+MDBBAAAEEEEAAgVYkkDFnVsKlzZgdWx7HHjlJevU4KurrduvyW+nf50YzpVFC4ZOor8eBCCCQHIGm+4k3cV2dz1KHdz/xxBPy4YcfWj861FsDkIWFhdZwa53TUYfJ6xBxeyi8ZjtmzBhrbsdJkybJRRddJBoA69evn7XSuPb+1OCX9qg8+ujYu5lrsG7ChAly4YUXWkFCDRZqEE7LpMOtu3btGrY3YhPVjWr3tddeK+ecc461KNF9991nBYb1xFGjRslJJ50k7777rlWvQw891JrXUns0fvfdd1bvzOHDh8tZZ53V4Do67+Zzzz0nP/74o9UrUgOin332mRUEvPXWWyW0B6iuGK+OGsy89NJLrV63GlTUa5SUlFivI/X01PM00Kq9NbW89nV0DlAd3q+LW8WTLrvsMrnkkkvkvffes4Ky2hYa9NXh7hoE3X333WuD6fo+0CCxDpnX98XBBx9sLWKkQeNZs2aJDvfXhZE0uK71sXu8xlMuzkEAAQQiCXhXrxbv9sXrIh0TzXbvqpXi2bZVgu0bX0Apmrw4BgEEEEAAAQQQaCsCXvM3umf7mh+J1Enz8C5eLAHzd2K06cD9npaiop9k1g9/li1bF5gOUzVrj+w43yv57YfKXmbe0NLSbmFHle44lmcIINAaBBIOgGolx44dawU8tTfismXLrF6X2ntPkwZDNTinCyHZw9KtHdv/o8E7HSr+zDPPyEcffWRt1UWFdNi79qbUn3iTLkJ0zz33yF133WUFDjV4qIstaWBPFyo6/fTT48260fN0Lkvt7am9HbUXpgZ4dREfTddcc42MHDlS/vnPf1rbdZ8mDRBrT1c9L9wK60ceeaQV1HzhhRdk5syZVj10CgINqNp5WxmZ/2gdtTep9sjVofP2MHkNSmtQWLdFCoCeeOKJVjbPPvuszJ4923quPX01EH3UUdF/S2aXxX7Ua2tg9e6777auP3/+fGuXBqq13uPGjatdGGrQoEHWIkgPPPCANdzdHvKu5dAg6imnnCL/+7//awVDNYAa2vvVvh6PCCDQegUyP/1YPKbHeLjUd+sWc4NqFsbzeiXLfPmWzOQt2DEPdSLX8ZiTs/79hgQ7dmqQjd98uaVfEmY1U4/2gPnyr/rgHSMxGhSIDQgggAACCCDQ6gUyJn8hsfaKdLrSXq9HSr1pktvIqu76BbFTKefVl8yXzbHdG+aai/cR/Tv9MNmatllK0s0X1hKQPF8Hyfd3Eo+YHp/ffyHbTGei6uwcq6jttxRLRgJB2+o995LqQw93qtrkgwACMQiYeYP1T0lnky70o8PMdfXucMO5I11Ne2fqoj662nd2dnakw+Larr0NdTi6BuK0x2oqJHVauXKlVR51Cre6+muvvWZNIXDCCSfIddddZw1f196P3bt3l2gWhiorK7OC0hqUjeZ420WHyf/yyy/WQkS9ezs7fFOHri9fvtxaTEl7nIart5ZDy6DvB31faBm0125LJ+0R61TShcB0QSmtZ6QFopy6VnPk07lzZyuQE2khreYog1PXaItto1NPFBcXO0WU1Hza3T1BvGZxPJLzAr6Bg6T8okucz9jkqPNg6xdwbekzrTX93jTWqHbb6Mia0Kl5Gjsnmn2x3ONFkx/HIIBAeAG9F9bRWE4k7ZxiTzflZL5OlC2ePLQuWicn79HjKYcT5zjZNtnPPyMZC2o6nDhRNvJwTqB6l19JxXnjnMswjpz4vYkDrZlOoW2ahk7k/tORHqD1i6gf3jqkOdakAa5kBbn05l9/UimpU+iUANGUTXtMxrIQkf4CaU/RWJNeR4eaJyNpb9do6qBl0MCn0wHYZNSJPBFAAAEEEEAAAQQQQAABBBBAAAEEUlMgKQHQ1KwqpUIAAQQQaA0C5WeOFU+Eni5r1661FlLLyMgQ7ambzJS2ZLFkTZroyCUqTjhJAmEWuNMRCToQQ+czbo4U3D58qzmuxTUQQAABBBBAoGUEqg4/UvyDd26Zi2+/qt6r6ajObdu2RSxH2sIFkmHut5xI1TsPFb+Zei8ZaUO1TypN5xxNXYMByd3+PJ5r+XcaEM9pnIMAAg4IEAB1AJEsEEAAAQScEwj03yliZiVmNiYdlqw9yf1xLsoWMfN6OwJmGpdMEwDVOTwTScGMTKnedz8xq7k1yMZrpo7Q+vhbyfQEDSrABgQQQAABBBBIOQG9l2rsfqo5CpxhRiJmmRGPmxuZQqx6txGSfteExO+1TIUqTj1NJMx8607UtcRMW1dZWWlllWdGlWakyJR6TtSNPBBwk4CZ1ZeUygI6R6YuchRuAalULjdlQwABBFq7QDC/g/gHDEy4Gr7hw8MGPxPOmAwQQAABBBBAAIHWLNDJrCHQrl3CNQi2y0ta8DPhwpEBAgikjAA9QFOmKcIX5MADDxT9ISGAAAIINL9A1dHHSvqT/4z7wkGz0FnlkUfHfT4nIoAAAggggAACbVmg8vgTJfuNf8XdC1RXdK484cS2TETdEEDAIQF6gDoESTYIIIAAAm1PwD9osFSNNsPX40xVRxwlwW7d4zyb0xBAAAEEEEAAgbYt4NtzLwn06Rt3JQN9+4lv1J5xn8+JCCDgHgECoO5pa2qKAAIIIBCHQOVJvxXfsF1iPrN6r31EA6AkBBBAAAEEEEAAgcgCZRf/SQJm6qFYU8DMMVo2/tJYT+N4BBBwqQABUJc2PNVGAAEEEIhSwKz0WX7u+VJ18CES9DS9JFIwPV0qf328VJx2RpQX4DAEEEAAAQQQQMDFAubeqfQvN4pv4CDRIe1NJT1Gjy297kYRcy4JAQQQiEaAT4tolDgGAQQQQMDdAjqX5/G/kep9Rkvm5EmSPn+eeMrL65gE8tqLz6xmWnXoYRJM0iqkdS7ICwQQQAABBBBAoK0ImHut8osuEe+SxZL9n7fFW7ixwbygGvgMdO0mFSefIoGdh7SVmlMPBBBoJgECoM0EzWUQQAABBFq/QKB7D6k4/UwRv1+8mzaJZ9tWEdMrVIdtBbt0sZ63/lpSAwQQQAABBBBAoGUENLBZds11pounT7xLl0pawXqrIP7uPSUweDA9PlumWbgqAm1CgABom2hGKoEAAggg0KwCZlh8oLtZ3Eh/SAgggAACCCCAAALOCpih7YFhw6wfZzMmNwQQcKsAc4C6teWpNwIIIIAAAggggAACCCCAAAIIIIAAAi4QIADqgkamiggggAACCCCAAAIIIIAAAggggAACCLhVgACoW1ueeiOAAAIIIIAAAggggAACCCCAAAIIIOACAQKgLmhkqogAAggggAACCCCAAAIIIIAAAggggIBbBQiAurXlqTcCCCCAAAIIIIAAAggggAACCCCAAAIuECAA6oJGpooIIIAAAggggAACCCCAAAIIIIAAAgi4VYAAqFtbnnojgAACCCCAAAIIIIAAAggggAACCCDgAgECoC5oZKqIAAIIIIAAAggggAACCCCAAAIIIICAWwUIgLq15ak3AggggAACCCCAAAIIIIAAAggggAACLhAgAOqCRqaKCCCAAAIIIIAAAggggAACCCCAAAIIuFWAAKhbW556I4AAAggggAACCCCAAAIIIIAAAggg4AIBAqAuaGSqiAACCCCAAAIIIIAAAggggAACCCCAgFsFCIC6teWpNwIIIIAAAggggAACCCCAAAIIIIAAAi4QIADqgkamiggggAACCCCAAAIIIIAAAggggAACCLhVgACoW1ueeiOAAAIIIIAAAggggAACCCCAAAIIIOACAQKgLmhkqogAAggggAACCCCAAAIIIIAAAggggIBbBQiAurXlqTcCCCCAAAIIIIAAAggggAACCCCAAAIuECAA6oJGpooIIIAAAggggAACCCCAAAIIIIAAAgi4VYAAqFtbnnojgAACCCCAAAIIIIAAAggggAACCCDgAgECoC5oZKqIAAIIIIAAAggggAACCCCAAAIIIICAWwUIgLq15ak3AggggAACCCCAAAIIIIAAAggggAACLhAgAOqCRqaKCCCAAAIIIIAAAggggAACCCCAAAIIuFWAAKhbW556I4AAAggggAACCCCAAAIIIIAAAggg4AIBAqAuaGSqiAACCCCAAAIIIIAAAggggAACCCCAgFsFCIC6teWpNwIIIIAAAggggAACCCCAAAIIIIAAAi4QIADqgkamiggggAACCCCAAAIIIIAAAggggAACCLhVgACoW1ueeiOAAAIIIIAAAggggAACCCCAAAIIIOACAQKgLmhkqogAAggggAACCCCAAAIIIIAAAggggIBbBQiAurXlqTcCCCCAAAIIIIAAAggggAACCCCAAAIuECAA6oJGpooIIIAAAggggAACCCCAAAIIIIAAAgi4VYAAqFtbnnojgAACCCCAAAIIIIAAAggggAACCCDgAgECoC5oZKqIAAIIIIAAAggggAACCCCAAAIIIICAWwUIgLq15ak3AggggAACCCCAAAIIIIAAAggggAACLhAgAOqCRqaKCCCAAAIIIIAAAggggAACCCCAAAIIuFWAAKhbW556I4AAAggggAACCCCAAAIIIIAAAggg4AIBAqAuaGSqiAACCCCAAAIIIIAAAggggAACCCCAgFsFCIC6teWpNwIIIIAAAggggAACCCCAAAIIIIAAAi4QIADqgkamiggggAACCCCAAAIIIIAAAggggAACCLhVgACoW1ueeiOAAAIIIIAAAggggAACCCCAAAIIIOACAQKgLmhkqogAAggggAACCCCAAAIIIIAAAggggIBbBQiAurXlqTcCCCCAAAIIIIAAAggggAACCCCAAAIuECAA6oJGpooIIIAAAggggAACCCCAAAIIIIAAAgi4VYAAqFtbnnojgAACCCCAAAIIIIAAAggggAACCCDgAgECoC5oZKqIAAIIIIAAAggggAACCCCAAAIIIICAWwUIgLq15ak3AggggAACCCCAAAIIIIAAAggggAACLhAgAOqCRqaKCCCAAAIIIIAAAggggAACCCCAAAIIuFWAAKhbW556I4AAAggggAACCCCAAAIIIIAAAggg4AIBAqAuaGSqiAACCCCAAAIIIIAAAggggAACCCCAgFsFCIC6teWpNwIIIIAAAggggAACCCCAAAIIIIAAAi4QIADqgkamiggggAACCCCAAAIIIIAAAggggAACCLhVgACoW1ueeiOAAAIIIIAAAggggAACCCCAAAIIIOACAQKgLmhkqogAAggggAACCCCAAAIIIIAAAggggIBbBQiAurXlqTcCCCCAAAIIIIAAAggggAACCCCAAAIuECAA6oJGpooIIIAAAggggAACCCCAAAIIIIAAAgi4VYAAqFtbnnojgAACCCCAAAIIIIAAAggggAACCCDgAgECoC5oZKqIAAIIIIAAAggggAACCCCAAAIIIICAWwUIgLq15ak3AggggAACCCCAAAIIIIAAAggggAACLhAgAOqCRqaKCCCAAAIIIIAAAggggAACCCCAAAIIuFWAAKhbW556I4AAAggggAACCCCAAAIIIIAAAggg4AIBAqAuaGSqiAACCCCAAAIIIIAAAggggAACCCCAgFsFCIC6teWpNwIIIIAAAggggAACCCCAAAIIIIAAAi4QIADqgkamiggggAACCCCAAAIIIIAAAggggAACCLhVgACoW1ueeiOAAAIIIIAAAggggAACCCCAAAIIIOACAQKgLmhkqogAAggggAACCCCAAAIIIIAAAggggIBbBQiAurXlqTcCCCCAAAIIIIAAAggggAACCCCAAAIuECAA6oJGpooIIIAAAggggAACCCCAAAIIIIAAAgi4VYAAqFtbnnojgAACCCCAAAIIIIAAAggggAACCCDgAgECoC5oZKqIAAIIIIAAAggggAACCCCAAAIIIICAWwUIgLq15ak3AggggAACCCCAAAIIIIAAAggggAACLhAgAOqCRqaKCCCAAAIIIIAAAggggAACCCCAAAIIuFWAAKhbW556I4AAAggggAACCCCAAAIIIIAAAggg4AIBAqAuaGSqiAACCCCAAAIIIIAAAggggAACCCCAgFsFCIC6teWpNwIIIIAAAggggAACCCCAAAIIIIAAAi4QIADqgkamiggggAACCCCAAAIIIIAAAggggAACCLhVgACoW1ueeiOAAAIIIIAAAggggAACCCCAAAIIIOACAQKgLmhkqogAAggggAACCCCAAAIIIIAAAggggIBbBQiAurXlqTcCCCCAAAIIIIAAAggggAACCCCAAAIuECAA6oJGpooIIIAAAggggAACCCCAAAIIIIAAAgi4VYAAqFtbnnojgAACCCCAAAIIIIAAAggggAACCCDgAgECoC5oZKqIAAIIIIAAAggggAACCCCAAAIIIICAWwUIgLq15ak3AggggAACCCCAAAIIIIAAAggggAACLhAgAOqCRqaKCCCAAAIIIIAAAggggAACCCCAAAIIuFWAAKhbW556I4AAAggggAACCCCAAAIIIIAAAggg4AIBAqAuaGSqiAACCCCAAAIIIIAAAggggAACCCCAgFsFCIC6teWpNwIIIIAAAggggAACCCCAAAIIIIAAAi4QIADqgkamiggggAACCCCAAAIIIIAAAggggAACCLhVgACoW1ueeiOAAAIIIIAAAggggAACCCCAAAIIIOACAQKgLmhkqogAAggggAACCCCAAAIIIIAAAggggIBbBQiAurXlqTcCCCCAAAIIIIAAAggggAACCCCAAAIuECAA6oJGpooIIIAAAggggAACCCCAAAIIIIAAAgi4VYAAqFtbnnojgAACCCCAAAIIIIAAAggggAACCCDgAgECoC5oZKqIAAIIIIAAAggggAACCCCAAAIIIICAWwUIgLq15ak3AggggAACCCCAAAIIIIAAAggggAACLhAgAOqCRqaKCCCAAAIIIIAAAggggAACCCCAAAIIuFWAAKhbW556I4AAAggggAACCCCAAAIIIIAAAggg4AIBAqAuaGSqiAACCCCAAAIIIIAAAggggAACCCCAgFsFCIC6teWpNwIIIIAAAggggAACCCCAAAIIIIAAAi4QIADqgkamiggggAACCCCAAAIIIIAAAggggAACCLhVgACoW1ueeiOAAAIIIIAAAggggAACCCCAAAIIIOACAQKgLmhkqogAAggggAACCCCAAAIIIIAAAggggIBbBQiAurXlqTcCCCCAAAIIIIAAAggggAACCCCAAAIuECAA6oJGpooIIIAAAggggAACCCCAAAIIIIAAAgi4VYAAqFtbnnojgAACCCCAAAIIIIAAAggggAACCCDgAgECoC5oZKqIAAIIIIAAAggggAACCCCAAAIIIICAWwUIgLq15ak3AggggAACCCCAAAIIIIAAAggggAACLhAgAOqCRqaKCCCAAAIIIIAAAggggAACCCCAAAIIuFWAAKhbW556I4AAAggggAACCCCAAAIIIIAAAggg4AIBAqAuaGSqiAACCCCAAAIIIIAAAggggAACCCCAgFsFCIC6teWpNwIIIIAAAggggAACCCCAAAIIIIAAAi4QIADqgkamiggggAACCCCAAAIIIIAAAggggAACCLhVgACoW1ueeiOAAAIIIIAAAggggAACCCCAAAIIIOACAQKgLmhkqogAAggggAACCCCAAAIIIIAAAggggIBbBQiAurXlqTcCCCCAAAIIIIAAAggggAACCCCAAAIuECAA6oJGpooIIIAAAggggAACCCCAAAIIIIAAAgi4VYAAqFtbnnojgAACCCCAAAIIIIAAAggggAACCCDgAgECoC5oZKqIAAIIIIAAAggggAACCCCAAAIIIICAWwUIgLq15ak3AggggAACCCCAAAIIIIAAAggggAACLhAgAOqCRqaKCCCAAAIIIIAAAggggAACCCCAAAIIuFWAAKhbW556I4AAAggggAACCCCAAAIIIIAAAggg4AKBdBfUkSoigAACCCCQUgJBn0hgvle2LK4W34aAVBVniad9UDw9gpI20i+enQPi4SvKlGozCoMAAggggAACCIQKBCpF/J+nS2BmmgS3eEQqzU+WuZ/rGBTvfn5JO9wnXiIuoWQ8R6BFBfh1bFF+Lo4AAggg4DYB37Q08f0nQ6TIK9Xi3159c+O8/Zn/kwzx9A5I+qlVkrZ7wG081BcBBBBAAAEEEEh5gepXMsT/pQmnBEzQMzSZIGhwqwmMrkwT/1sZkna0TzJ+Vx16BM8RQKCFBAiAthA8l0UAAQQQcJdAsEqk+rlMCXzX9D+9wbUmOPpQtgSOrZb0U6rpDequtwq1RQABBBBAAIFUFTC9PivvyJZgQRRDdfwe8X+UIYEfvZLx10p6g6Zqm1Iu1whE8VvrGgsqigACCCCAQFIEgqYjZ/UT0QU/Qwvg/zhDfG+a3qIkBBBAAAEEEEAAgRYX8N7XKbrgZ0hJg6vTpPqW7JAtPEUAgZYQIADaEupcEwEEEEDAVQL+j8z8UD803fMzHIr/MzPEalZauF1sQwABBBBAAAEEEGgmgbxZXSWtIDOuqwXXm9E9L/Cldlx4nISAQwIEQB2CJBsEEEAAAQTCCeg8UL4PE7vh1V6gunASCQEEEEAAAQQQQKD5BTwVXsmf2iOhC/unmi/ESxLKgpMRQCABAQKgCeBxKgIIIIAAAk0J+Kebnp+6KmgCKVjoNfNH0Qs0AUJORQABBBBAAAEE4hbInd9J0iriG81Te9GgmRNUF8IkIYBAiwgQAG0Rdi6KAAIIIOAWAf/3zgQuAw7l4xZ36okAAggggAACCDglkLM435Gs/D84c1/oSGHIBAGXCST4FYbLtKguAggggECzC1S/a4Z/L6r5vq5r+UAJBkW8Xo9UZWc1e1niuWDwF2e+a9R5QIOFqVvn9ZmlFo+2T3V1w3J69/VJ+hh/PIScgwACCCCAAAJJEKi4zizMU5bYKJVIxdLBL8WeLRII5EQ6pNVs75i3k2RsbnhvE1cFijxScVnLmrSWtvHkByXr9oq4mDkJgXACBEDDqbANAQQQQCBlBIJrPBJYWPNteZbk1ZbLLKzurlSxwyEVK14hocHNhr0bPIO1xUKPScVaUCYEEEAAAQRcJGCCcRJITgDUfB8q+iOSnPytrJvpP5nlTgYsjUd5MxU8wmVaS9sw/32EBmRz3ALOdEuJ+/KciAACCCCAAAIIIIAAAggggAACCCCAAAIIJE+AHqDJs035nB988EH58ccfpU+fPnLzzTebIaWR4+EvvPCCTJkyRY477jg55ZRTUr5uFBABBNqOQMZ5VSJjzY9Jy5evMEOpApKZmSF9+/ZtFZWsuitbggWRP1+jrYR3V59knF/jEO05yT6uzHTonF6cLt9tTTPD3LKlwrzubkaI7ZRWJod28knf7Jq+H1Y5HBo5luw6kT8CCCCAAAJuEcj8u+mK6EtObbOzcyUvr50UFm5MzgWaMde1m9dI/mt9JXOjAz1Bzb1R5nUt2wU0kbb5odQrrxZmyqLKNNlq7vvSTYfWTmlB2b+dT/6ne5V0cTLClNmMjcylXCHg5NvTFWBtqZKrVq2ShQsXWj+77babnHbaaRGrt379euu40aNHRzyGHQgggEAyBDy5O3IN5PmsAGgg0yueDju2p/Iz7wi/+Cc6EAAdGUiZOleZ0ewvL8+UZ5ZlyjafPbQtdFKCbLlnhciRParlyiGV0r9dSCA0lRuLsiGAAAIIIOAiAW+v5FU23dy/ZXVIF68DMcPklTK6nANBv1QM3upIANS7s1+8/aK7brKOiqdtvtvklRt/ypH1ZkqmOtMamFu8DeYWcEFxmjxXnCn7dvbLvSPLJZ/gZbKaj3wTEEj8L7IELs6pqSPwxBNPyOrVq1OnQJQEAQQQaCMCafuar8cTTRlBSdsrSV00YixboZk5/w/f5soDi7NCgp/hM/l8Q4ac9nU7mVTQcE7Q8GewFQEEEEAAAQQQSD2BsuHFZk7TRL/QNfdzx6XG/Vwswv9ckiHjZuaa4KeGj+wvvsPl4JFvNqfLkV/mybwthJrCCbGtZQV4V7asf0pcXYe+V1ZWyoQJE6yeVSlRKAqBAAIItBEB76CAeEcldrObdoQvJXp/bqsWOd8EP3/cEn1As9zvkSvn5MhXG6M/p400PdVAAAEEEEAAgTYiUN2tQsp2LUqoNp4+AUkbGjpiJqHsmuXkZ37JkH8u1XmMGgt81i1KhVlY6+xvcmVVad3tvEKgpQUIgLZ0C6TA9XVOz8zMTGs+0DfffDOuEm3btk3mzJkj7733nsycOVO2bNnSIB89ZtmyZaLD6esnn89n7dP9Or9f/bR27Vprf0VFRe2uzZs3y7Rp0+Tdd9+VGTNmhM3XPnjlypXW+cXFxfYmHhFAAIFmE8jQOUw7Nvxsi6YAnv4BST/RRB5TIP3tp2xZXhb7rUPQ3DRf90OOrCuP/uY5BapLERBAAAEEEEAAgVqB4iPWSjAjvvs5SQ9KxlWVtXm1hic/m16cD5oRP7EEP+16+YIe+Z9v2tkveUQgJQSYAzQlmqFlC9GvXz+58MIL5ZFHHpEnn3xS9t9/f+nfv39UhfL7/fLSSy/J888/L/rcTtqr9JxzzpFzzz1X0tNr3mYrVqyQiy++WLp37y5vvfWWfaj1OHfuXLniiius548//rjsuuuutfs13wsuuEDKysqsAGt2drZVVs1DA6ehSecovf7666Vr166hm+Wqq66SgoICGT9+vIwdO7bOPl4ggAACyRbwdBTJvKJSqh7IFtkSfRDQ09ss+HRZpXhSYB6lmZvT5IuCjLipSk1P0EeWZMntI3Z8kRV3ZpyIAAIIIIAAAgg0s0Agxy++ccWS8WQnEdPLMepkFgnS4KfX3A+2pvSXH819aww9P+vXrajaK0+bHqQXDEqNL/Lrl4/X7hOIvRuH+4xcUWNdAGnEiBFSVVUld9xxR9hemOEgbrrpJnnmmWekffv2VnDxzjvvlEsvvVS6detmBUVvueWW2tOGDx8uHTt2tAKRGgwNTd9++23ty9mzZ9c+1yfz5s0T7T06cuRIyc/Pl9dee01ef/11K5B6zTXXyD333CN//OMfZfDgwaL5XHvttXXO5wUCgtU7lAAAQABJREFUCCCQCgLefkHJ+muFeIfv+LIocrmC4t3PJ5k3VIinU6LzTUW+Six7Xl4Rf/DTvs4Ha9OlOLUWsreLxiMCCCCAAAIIINC0wLBqybzZrOKeH2VP0E7my+zbyiVtWJTHN12CZjlinanicrPie6LpJbNoJgmBVBFI/B2dKjWhHAkJaI9N7TmpQ+F/+ukneeONN5rMT4edf/XVV9K5c2d54YUXrJ6VBx54oJx55plWULRnz57y5ZdfWkFJzUyvccABB1j5hgY8dYMOm9dra6ofAJ0+fbq1/eCDD7YeP//8c+vxL3/5i5x00kmy3377Wb1N7733XtHeoUuWLJHFixdbx9j/ue++++S5556T4447zt7EIwIIINDsAhrMzPzfSsm42gRCTYDTk1evCOYmOe0Qc2N9U4VkXlAlHv3iPQVSpYnZTi9MfNCIWcfezAWaeD4pQEIREEAAAQQQQMClAt4+Itn3VUj6780onW4msOmp92W1ee3paaYwOq9Ssu8x93zdWh/U88vjG/pev6ZF1R7Z1LpG/tevAq/bkAB/hbShxky0KjoU/qKLLpKHH35YnnrqKStY2dhQ+Jdfftm65Pnnn28FQUOv36FDBznqqKOs4fHaW1OHpmvSIOaHH34o33zzjWivU006L+fChQutfT///LPocHjtiWoHRHWeT012ANQeaq+9SEeNGmXt0/906dJFnn32WcnLy5NOncywhJC00047hbziKQIIINA8Ap+tT5d31kToOWk+vjJHp0tGVUAySvxSmiNSna7Dqcw/zRok3Ng8ZYzmKmVmthGd0N6JpBPpf7w+gokTF3Aoj8wMn7Xaa3W1aZgwqWd2QG7alTv6MDRsQgABBBBAoMUF/mdGjqyKY97ycAXv6h0g7b01U6+tXZ0rlcHtCzvqrdFRNWfkVAelk+k1udncNlRkbO9nVmWO+yJcji27zePxiMdTYkZ9Rp6jc6sJXDqTPHLClHaSmaSudx6P+Xrd1EVTIGiCtsHW3eM0XNuc2b9KLt6ZaQSceD8SAHVCsQ3lceqpp8rkyZOtBZF0VfjHHnvM6rkZrorLly+3NmvPTl0AqX7SgKSm0OHue++9txXY/P7772uDnLNmzbKO0306X+ikSZOsYe8a3Fy3bp3odYYOHSo9evSwjhszZozVy1N7fGowVXuA6s8uu+wiGsQlIYAAAqkisKrcK1Mb7TmpPQa2Bz1dEktbY0z0J/WT3Zsj/K3SgFwdyuaSRkv9xqKECCCAAAII1BFYZoZvb/M5c79RJJEDhfZFdX34tfZ3pq0mVuWMj20Q6bHU75XSaGaAipRB1Nv1ntqpwG3UF03SgTvaZlmpBtxbzZsqSR7OZBv+rt6ZvMmlFQpoMPOGG26Q8847zwpCau/N3//+9w1qonNy2iu933333Q32h27QxYfsHp06RF0DnTqsXXt66nN7OPxee+1lvr3xWAFQHQavAdD6w981Xy2bLoikZZs/f771o8PbdX7RQw45xNpffxGk0PLwHAEEEEAAAQQQQAABBBBAAAEEEEDAPQIEQN3T1lHXtG/fvtZQ+Iceekiefvrp2nk7I2WgK6vbK71HOiZ0uw5l18Dmd999ZwVAdf5PDVjqMHUNwGrSXqHjxo2Tr7/+2nptD3/XFxok1YWWTj/99No5RjVgqkPp3333XZkyZYrcf//9MmjQIOtc/oMAAgi0lMBpfavk6B6Rv7HtYL64CQQCsm3r1pYqYlTXLa7yyFnfNN37IZrM/jCgUk7rF9mkqTyWVWyQ70oXy/qqItkWKDND0nKlZ2Yn2afdzjIwu2dTp0e9v6m2Sd/xxXzUeXIgAggggAACCDSPwDP7lMu2+G836hSyrHCteP01oz7SOvSQjOzcOvub80VVsFombp0pP5Qtkq3+EvGJX3K9OdIvs4ccnb+v9dhUebJMp6R27drJ5k2bIh76yJIsmVPsTLjor7uUy8C85CwClZdnhtdn1Uyav3XLFvH5aqYqiFixFN8Rrm36t0uOXYpTJKV4zryjk1I0Mm1JAXsovPbS1KHw9efQ1FXfdZ5P7QW6zz77WEPUoy2vvRCSzgN6wgknWKvCH3PMMdbpOoS9e/fuonOBbt682VoQqXfv3tYK7/Xz15XmtZz6oz1MNaD6j3/8QwoLC+W///2vXHHFFfVP4TUCCCDQrALtzVSX7TPsodQNL905z2MCoB4p9kU+puFZzb+lb25Qhrb3y6JtOgQnsXRsL59ofrGm77YtlwfXTJSfy9eFPfXlwknyq5yecmWfI2Wf9gPCHhPLxtbSNrHUiWMRQAABBBBwi8Au0a7SHgXIytIqqaysso7smV8t7ds3f0CqIuCTW1a8J58V/2zmKK+btgSKZZ1vnXxb9r30yuwgt/U/SUa171/3oJBXuSZ+26FDuqyTyPU4119lprlLPFyUYRaEOn2n5AUlO3TwSm5uTTkLzT13dXXkOoUQpOzTaNomZQvfCgpG/4VW0EgtUUTtZamrwmdlZVnBSJ0XtH4aOHCgtUlXeg+XNAg5duxYue222+rs1lXjhw8fLkuXLhV7RXcdCm8nfa4LHT3zzDPmA6y6dvEj3b9hwwb505/+ZA3LLympmexYt+uCSboCvT1cX1eCJyGAAAIIOCdwnAlcJpoGtPPLr2L8gyQYDMpjayfLRUtejhj8tMs1v3y9ddyjayeJnkdCAAEEEEAAAQRau8Cqis1y7E8PyKdhgp/167auaouMW/KiPLHuq/q7Ynp9eHe/Wbgo8XupUZ2aZfLPmOrGwe4VIADq3rZvsub2UHg9sLzcLGlXL+nq75r+9a9/iS5qFJrWr19vrSa/cuVK6dWrV+gu6/lBBx1kPb722mvWo87/aSc7GPr+++9bm0KHv2vv0DVr1sjq1avFPtc+Tx9nzJhhvdxzzz1DN8vUqVPls88+sxZUqrODFwgggAACUQn83qxA2TUzsW/VrxhS03siqgtuP+i+NZ/J0xumxnKKPLNhmvxjzacxncPBCCCAAAIIIIBAqgkU+crk9AVPmuHuFTEV7Yn1X8mTCQZBzx+Y2GKPHtNXdcKIhnGEmCrCwQg4KJB4n2YHC0NWqSegw8u1h+cPP/zQoHC6SNFJJ51kzbt5+eWXy6GHHmoNVV+0aJE1HF2DptrT86yzzmpwrgZAn3zySWsxo/79+4sOZ7eTHQDVefF0YaPddtvN3mXN/3nJJZfIrbfeKi+88IK1YJIOqa+srLQWU9LgqK4Wf+yxx9aeo090TlBdjEnnKx0wYECdfbxAAAEEEGhaIMeMfr99RIVcPCvHDJiKfYXNE3pVyxE9YutF+knRPHll47dNFy7MEf/a+J2MyO0jx3be8W9ImMPYhAACCCCAAAIIpKzAOQuflcpgbPdPdmUeN0HQ/fMHyYh2fe1NMT1esnO1fLQuQ1aUxTMFUlDGD66U7jXTc8Z0XQ5GIFkC9ABNlmwbydceCq+rt4dL11xzjdx0003WIkaTJk2yFk366quvrCHsGjzVOTlzcnIanKrD53VuT02hvT/1dadOnWrn/NTgZlpa3Q/co446yrqm9lDVHqbaE/Sdd96x5v7UYfC6cFO4XqeaNwkBBBBAIH6B/bv65eZdKyXNzOcUSzqoq5m3arfYei5UmrmudM7PRNKDa78QzYeEAAIIIIAAAgi0NoH/bvpB1lQVJ1TsG5b/J6HzX9+/TLrEPAIoKDp10sUmgEpCIJUE6AGaSq3RzGXR4GQ0qU+fPtbw8UjHakBSf3RBJA1I6gJJGoDU+UMbS6+//nrE3c8//3zEfbpDr3fkkUdaQU+dFzQvL0+0J6m9inz9k9966636m3iNAAIIIBCHwG/7VkufnIDcPC9b1pQ3/j1qugmUnjegSi41Q9/TYuw0OrF4vqyv3hpHCXecssGc/7nJ5/jOI3Zs5BkCCCCAAAIIINAKBLQHZ6JJA6jLyjfKwJwdIy5jyVPXF/rskFK5YGaOzC7SjkmN39B5zbD3y4ZUyrhBBD9jcebY5hEgANo8zq64iq4KP2JE8/2Rqb1Tdeh86PB5V0BTSQQQQKCFBUZ38ct7B5XKO2sy5ON16dYNceiw+L4mQHpYd5+ctVOVCZbG1lvUrtrkLYvspwk9Tt6ykABoQoKcjAACCCCAAALNLaCrvuuCRk6kFwpmyC07/SburNLN993Pjy6XH4q8cueCbFm4zSu+YGggNCh5JrJ0QJeaET/6nIRAKgp4zCqp8f1lkoq1oUwIJCig851G6i2qq9dfd911CV5hx+k6rYAGcfVXsKIitqGhO3JJnWeZmZlWYaqqqlKnUHGWhLaJE64ZTps1a5bo/MD6fhs5cmQzXDG5l3Dq92Z60WJ5Zc3XyS1sI7kHzE1wdaCdBILpkplWZnp7Jv458NXmhVLqT2zyfS1yu7QsGdN5WCOlD7/LHlGg77dUS2f13l8O7Dwk6mLZn2laF50z26kUboobp/ImHwTcJLBt2za58cYbI1b54osvdmwO+4yMDElPr4lO6OdBKn7GRYQIs0PronUKt2BrmMNTelNbaZtbFr0jn278yeoo6DG9BdtCuKGzP0vaS83fOms9pVLtTf69gU7hU+QrdeQ9my5es5Bl+x15mdhlom3j82eKz9/B5OMzv4NbxOtJvsmOCoQ8214X3ZLs91qGN02+3P+GkIs7/7StfqZpvMOp9knk/pPYvPPvWXJsxQI6hP+TTz4JWwOds/SWW24Juy+RjRoETeSXOJFrJ+PctlQX2iYZ7xDn8mxL77VE67KhsEQ+2jjXOdw2lJMGUduazeE9do3r3w0N6ib6XmtDbw2qgkDKCGggMtL9pxby7LPPTsrvblPTVaUMUBQFaWufba25beaXrpP1DvVcjKLpm+WQ9c1yleRdxGeWr0xOm2ysKbSLRrs312dNc10nee+6ujlHWlOm7lHJf9X45F3Jvz5XQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEkibAEPik0ZJxaxQoKiqSgoKCsEXXYSm6wJNTqUuXLtaiTX6/XzZv3uxUti2Wj84Bq93at25NbNGSFqtAyIW7du1qTU/QltpGh7jpELvWnhYuXFg7BH7w4MGtvTqivzdOtM1WX7n5Zr9lf/c6depoDafym/eaLooXmirL18rWgilSUbpCfFVFkpaRL1k5fSS/20GS035Q6KG1z6//5W1ZXL6h9nW8T4bkdJc7Bv0u5tPbt88zbROU0lJnhp/FXIBGTuiZmS/56TmNHFF3V7I+05iDu64zrxCIV8Dn88nSpUsjnq73n3of6kTShUPtnkV636vXbs1JexWpz8aN23uiteLKtJW2WVy2QZZXbLKmK8ox7bOlDfxtULlxi2Rs7+UY6Jgt6bmNL/brxNtwY9U2uXPVR05kJYOzu8klfQ6rzUunYGorbZOZXi1bV7wlZYWzxWfed5o8GR0ls/Ne0r7vyeJNz62td6JPMjxeObTTLolm0+j5bekzTT+b7Z6fTv57k8j9J0PgG337sdNtAp06dRL9iZTWrVsXaVdC21v7zadWXoOf+tNW6qLD3zW1lfq0lbaxGmX7f2ibHRq5kiGDMrvs2NACz3q271n7xUFBec3tRfGWn+XHeXfIho3hVzFds/AB6dRxdxkx/Hrp3u2AOqU+JH+IIwHQg00+8dh0btfZCk4XVxfXKVeqvIjn/d8WPwdSpT0oBwKJCOicb8OGRZ6ruLCwUKqrnRljqp8DdtIveuP5LLHPT4VHew7T1l4PtWwrbTPQ3I/oT25urvVF7zpJzt9Pzfn+W1m8UioDNXNo92zX09FOMY3V4/7Vn0llMPEvKc7ourcclj+09lJtoW0qKgpkxsxLZd2mb2vrpWvU16SV4tsyV4qWPSc9uo2Rffd+2ATkO9o7E3pM9mdNW/pMs+ui4Kny7w1D4BN6+zt7cluYvNtZEXJDAAEEEGitAkuXvSgTvzwhYvDTrldR8Vz5avrvZa4JlAaDOybQP7rTcPuQhB6P6bRrQudzMgIIIIAAAggg0BIC+7QfkPBl08z4nJO7jEo4n1TKYH3Bl/LBp/tLYUjwM1L59Ev49z8ZbY6dGekQtrtIoFkCoHfffbdccMEF1s/9998fFe8NN9xQe868efOiOsfpg1atWuV0lg3y028Q7rvvPjn99NPl6KOPliOOOELmzm16IYtwZbv00kstM+1e3JIpXNkuu+wyq2ybNtV0S2/J8nFtBBBAAIHkCixa8qTMmfs3E9D0R32hRUsel9k/7FgFeWczdP3YBIOXx3QcLjoEnoQAAggggAACCLQ2gb/vdKJZv71mVFq8ZdfgZ7pZBLGtpIKNM2Tq1+eae8zoe8YGTO/dyVNPk6Kin9oKA/WIU6BZfhNWrFghOm+b/rz//vtSVlbWaHGXLVsmU6ZMqT2nqeMbzSyOndo99/HHH5dx48bFcXZspzz55JPyzjvviA6t1jkhO3fuLH369ImYSWNlW7x4sWWW7G7ZkQrXWNkWLVrUomWLVGa2I4AAAgg4K7B67ZemN+eEuDJdtuJVWbbiX7XnXt3nKOlh5guNJ3XPaC9X9z06nlM5BwEEEEAAAQQQaHGBTmb+yvG9Dom7HF3T28n1fY+N+/xUO9HnK5OpM842xdoxlUf0ZQzI5GmnmemNog+cRp83R7YWgWYJgNoYaWlpUlVVJVOnTrU3hX2cOHFi2O3NtbGkpEReeeUVx+baaazc06dPt3aPHz/eCoS++eabViA00jnNWbZIZYi0vbGynXTSSfK73/3Omgsm0vlsRwABBBBo3QI6hH3a19eaSsRzY1pT9x9/vtv8+1tiveiSkScPDjpDOpsb+FiS/sGg53U155MQQAABBBBAAIHWKnBBz4Pk1512i7n47byZ8uqwP1qL7sZ8coqe8N3sq00Asyru0vn9ZdYIpbgz4MRWL9CsAdBRo2rmnpg0aVKjcBoA7dixoySyulOjF0ihnfaKhQcffLC1eEQKFc3RolxyySVy5ZVXNtuE0Y4WnswQQAABBKISWLX6C9m0+ceojo10UFXVZlmx6q3a3UNze8jLw86XUe361W5r7Ike9/KwcTIst2djh7EPAQQQQAABBBBoFQK3DzhZ/tTr/9m7DwArqrP/48/2pSxLryIIiGLDLooN7GJ/NTEmdoO+1iRGo381JsYklkSNxqixvWrUmFhQo9gbNkBAgooNRWmK9LJ97/2f3+Asd+/eu3vL7O7s7vcky947M+fMmc/gMvvc55yzb8rD4UcU97Xnt/6Z9S5sXx8EL/7m+azvV+wzZtaN0UCbE2jRVeDHjh1rc+bMsalTp9r69eutS5eGGR0aJr9w4UI76qijvOMaE9WKiJ9//rktXbrUGzY+fPhwL3CarM6KFSts7ty5pnr9+vWzoUOHWv/+9X9B0r7Fixd7TWglPg3HVxkyZEjKn56k0i+do7Kysm7lRfVNQ8g1BL60tNQ7Z/wf6fRNbanvGnreu3dv23rrrRN6++fQCl2au1Oeqrv55pvbpptuasraTaU01TdNg6BzDB482LTSpcrq1atN163+lZSUmDJIZ8+e7WUJb7/99vVWY1fmsOaC1d+NTTbZxEaPHp30fmR7LalcL8cggAACCDQU+GL+kw03ZrBl8Tcv2IhhJ9fV7F9YanePPNleX/2pTVo2y95d+2W9VVGLcvJtTMlmdmTvHWzf0pF19XiBAAIIIIAAAgi0B4HTXCbosW419998/ZRNdc9B5ZHqepelmUKHFPWyCwbuZ/t0b3/PQgsW/SetueXr4cS80Xyg37nFk/r02jVmKy87ikCLBkA7depke+yxhykD9K233vIW/YmH9oe/77///kkDoGvXrrU777zTnnzySS+o5reR6yb3Pf744725OwsLC/3N3ve//vWv9thjj9UFHP2du+66q1166aVeEE7b7rvvPps0aZK3W4HAk046yXv9/PPPNzl8O51+/fa3v7WPPvrIa1t/aJEgFQ2F//GPf+y9jv8j1b4tWrTIfvWrX5nmBPVLTk6OnXrqqd6Xv83/rkCp+jNv3jx/k/d9s802s1//+tc2YsSIetsTvWmqbxMnTvTmfn300Ue94LPamDx5st1666128cUXe0Hs+++/v979POaYY+znP/+5vfvuu14/ysvL6049atQou/baa+sFSbUziGupOwkvEEAAgRYW0NxGs/57eQufNZjT6d/4r77O/pN59WbZ8uk2feYvGnSss9tygvv6ofta5fIg1rmFAbq64falbuh9/uql7pO1qTa9Qa3MNhQVFbkH7aj3oVy6LYza4gLr2mVIutU4HgEEEEAAAQQQSCrQLb/Ybhj2A5sx6xKbv+xdW5FXbFWWZ91dUK9HtNJbLqls0UM2OWkLG3YobpLjvmrdgsxtpVRWrgisq2+9e7oVFXYPrL0gG2quezOw/4E2etsrguxqm2yrRQOgEho/frwXAH3llVcaBED1i4YCoH379rXtttsuIaiOufzyy23mzJneXJnHHXecl1U4f/580/yZDz30kBdYvPnmm+uGlP/zn/+0Rx55xAYOHGgnnHCCF4BTZqT6MG3aNLvooovs3nvv9c43btw4b+i9Aqz6y3fuued62+MDqvGdS7dfWvVd2Y8KACrQesYZZ3gB1mTXrfOl2rcLL7zQy4SVzYABAzxvZd7ec889Xoap5uP0izJidY3KsNxnn328LwVLlaX73HPPmQKXt912m22xxRZ+lYTfU+1bosoPP/ywKWir8+v6dW59Pf74417A+tlnn7Xdd9/dFKz+9ttvvblS1e+77rrLu3d+m0Fdi98e3xFAAIGWFqh1D7AMzTH3YVjqDpotdMOMoS19t5Kfb9jQnxAATc7DHgQQQAABBBDIQmDZyvcsWval9Yhpo/FlpmMO5KWLMazxvjoSxbIV73Wky016rS0eAFUgS1kiCjzGD4P/4IMPvExAZXEqCJeoKItTwU8FM7VSe48eG/6z33vvve2QQw7xAnbvv/++KWg2YcIEr4mXXnrJ+37JJZeYPw/pmDFjvP06l4Z9K1tSw7533HFH01B6PwCqIGIqJd1+7bfffl6zd9xxhxcAPfzww73gZGPnSrVvmj9V7WpouYquQavNP/DAA56LHwBV0Pamm27ygp+nnXZavexQZeDKQQFaHaMgaGMl1b4lakND7zVH6I9+9CNv97HHHmvXXHONPfPMM/bUU095GbHKjPXLyJEjvYxQZYb6Jchr8dvkOwIIIIAAAggggAACCCCAAAIIIIBA2xdo8QCohpRpLlAFJbUa/EEHHVSnGDv8vW5j3AsNe1dRxqQf/PQP0aJJCuRdd911pqxPPwCqDEsVzUPpB0D1vlevXl5WZNeuXRu0pf3plEz6lU776Rx7yimn1AU//XrKrlQA9JtvvvE32axZs7xsWQVK/aH+dTvdiyOOOMILBCswrfk3NY9ocxTdBz/46bevOUAVAFWJ75vfDy0gVePS9jWnaFiuxe8/3xFAAIFMBAryS2zcXhumYcmkfmvW0c/yN9/+pX2zdGrW3Sgu7m+773J7Wu3kVlRZpy8WW8Hy1Za3vtwihQVW062LVWw2wKp7JZ5bu7ETlJZ2c5moUdP0NumW0m7tb+6tdA04HgEEEEAAAQSaR2DH0X9wzycb1irJ5AxFRYVeUtqqVaszqZ60TtGKtdZjzhIrWlVueZW1VluYa1WlxbZyq/5W0S+7IeeLFj9r3373WtJzp7Nj0IBDrV/ffdOp0mLHNte9KWWOfO8etngAVGdV9qMCoBqC7gdAFaTUey2Sk2y4tYJdyhZU0XDoRMXfrsVy1KYW8VF2qLI8//znP3sZkMr+1NeWW27pnS9RO+lsy7Rf6ZwjnWO1YFN80eJKKmVlG5PjNW2AiobJ//e///Vex/+heUC1MJWCx37gMf6YbN8rmze+aKEjFf1C3bmzZn3bWPxrUdan7rECoGG5lo295BUCCCCQvkBubr716rlD+hVDUKN/v/42bNhRgQRAB/Qbl7JDzupVVvTi85Y/c4bluMX2EpXaAQOt8uBDrXaLLRPtTrhN/9ZoUb3CglUJ97MRAQQQQAABBBBoDQEt4JPNIj76/VoLLy9ZsiSQ7ud99IEVP/6o5bhFjeuP43WJaN9UW99P1lrEjQKunHCE1ey8S0bn7NN7V3v+5X0zqhtfaYfRv7fiog3xkfh9rf0+6HvT2tcTtvO3SgBUQUqtAD99+vS6YfDK4Fu5cqX5w7MTQWnldAW8NIQ+2UrpygJV0FNBSc0XqeCaMiIV+NM8oJonUl+a81NDxZUZqf3+cPFE521qW6b9aqrdTPdrhfv44k8poKChX/xgsuYHPf/88/3NCb9rjs7mKgrAxhe/v8rOjS/+vtjtYbmW2D7xGgEEEOhoAiOGHWPvTL3MLR6UOBCZqsfgQYendGjeJ3Ot00P/sJzKykaPz1uy2Drfe5dV7TrGKo882tyDQqPHsxMBBBBAAAEEEECgaYHiRx6y/Fkz4wKfDevlugWNix99xGrfn2XlZ0xseEATW0q6bmaFhT3c9H0rmziy8d2digeENvjZeM/ZG4RAqwRAtaDQXnvt5S2yM2XKFDv44IO9xY90QZp7MllRpp+KgqAK5CUKhGmfvlQUCFXRceecc45p4aHXX3/dm39U84iuWrXKW0lefbjxxhtd5sow7/h0/8i0X+meJ9XjtXhTOkXTAmhagsaK5t1srpJoganYQG06523ta0mnrxyLAAIItDeB0m6b2agtTrGPPr4n40vr03t369un8X+T1Hj+hy7b4B/3WU7MB3tNnbRw2ruWs369VfzkJD0cNHU4+xFAAAEEEEAAAQSSCBTfd48VzP0oyd6Gm/Xklf/5p9b5r3+xsnMvaHhAE1u22/oKe2/WL5o4qvHdmj6A0nEFWiUAKm4Ng9cq46+++qoX9FRgcsSIEZZo+LZ/e5TZqKCmVizXCuoaHh1flPXpF2V4xhZlh2qBHX2pDWWg/ulPf7Jly5bZ008/bRdckP5/hGo/237F9rElX2u6ARWlWf/whz9syVMHfq72dC2B49AgAggg0IICY3a5yr76+iVbX/Z12mctyO9mqTyY5iz7zpRxkE7w0+9MwYdzLPLqy1Y1PvkHrv6xfEcAAQQQQAABBBBoKFAw5XXLTyP4GdtC3sIFVvTUJKs84qjYzU2+Hrrp/9i8L++zlatmN3lsogP69t7TBvQfn2gX2zqIQHqpggGi7LzzzlZSUuIFIZWBqUUGGsv+1KkV/Bw6dKjXC3/BJO9NzB/+9lGjRpkWXFJA9Nxzz/UW2Vnn5qTwi7IOlfXoL76jOUL94mdQppqFmEm//HOl+z3dvjXWvub3VJk9e7aXDRt/bEVFhU2cONHOPPNMU8ZsUyXIvjV1rvj9QV9LfPu8RwABBBBITaC4uJeN3e1uN7yob2oVvj8qL6+zjdn1NivpOqzJekWTn7Ec90FmpqXwlZcsZ02wE/9n2hfqIYAAAggggAACbUrATTdY9PzkJoe9N3ZNBe+8pQVKGjsk4b5xez1uGsaebunaZZjtPfbBdKtxfDsTaLUAqIaNa/7N6upqu/nmmz3WpgKgOkirvKtoRfPYoKW2ffzxx/bwww/rpZflqe99+/Y1zV+pRZG0Mnx8effdd71NO+64Y90uBU5VNJReK42nUtLtVyptJjomk74lakfbNFxcq60rMHz99ddbZdwcarfffrs3X+qXX36ZdGGq2LaD7Ftsu6m8DvpaUjknxyCAAAIIJBbo5lZB32+fp623m6Q/ldKtZHMbv/ck69dnzyYPz3GjNgrc8PdsSo57cC942z14UxBAAAEEEEAAAQTSEih4/VXTs1Q2RaN4ip59Ou0mtGDoIQe8bX17Nz1dkt/4gH4H2MH7v+q/5XsHFmi1IfAy1zD4//znP94Q9G233dYbSt7UvdCK7uPGjfOGziszUW1o+LNWKVf2pxY/0oI+Bx54oNeU5v88++yz7aqrrrL77rvPq7fHHnt4wb5p06Z5wVENYdc8pH5RdqiG1y9fvtwLuGr/Nddc0+hCSen2yz9Xut8z6Vtj5/jFL37hZci+8cYbdopbDEpZsQpOv/XWW97K6vK75JJLvEWrGmtH+4LuW1Pni98f5LXEt817BBBAAIH0BDp16m/77vlvW7TkBfty/kO2dNlbblX12KzNXBcg3dmGDj7OhrghTTk5qS1MlO9WGg2iaA7RKrcyPAUBBBBAAAEEEEAgdYECt5BRECX/47nW+DKWic+ikad7j33Ili1/z2Z/cIWtWv1xgwU49VxZ2m1L22n0ddajxzaJG2JrhxNo1QCosvY0T6cWI0ol+9O/OwpmTpo0ye6++26bPHmyt1lBOw17nzBhgvflH6vvBxxwgPf2nnvusa+//tr70gZlLCrgpwBf/Hyhl19+uV199dVeEFT9mz9/fqMBULWXbr9UJ5OSSd+SnUdDx5VNe8MNN3hBz0ceeaTu0M0339wbAj9mzJi6bU29CLJvTZ0rfn/Q1xLfPu8RQACBdiMQibi5l54I9HIqO3U2jYXS9DFFbqVPv2hA+zA73GrsIFtvK92D7nortM7WxbpbwbJis2XugFmT/MOb/J732WdNHpPKAXnfLbWix/9tlmThwNqi4g3XUpXJo3kqPcj8mJrRO1jtZk1PFZD5GaiJAAIIIIBAOxdYs8Y6331HShc5qFMXqy52zyyulKxeZQWRDYsup1Q5hAfl5uTa+rxc65xhFmeue4YKouS4kaidb7w+46Y2dTU3y3VzuuccYKvzVtjqnBVuS9RKarpbt9qeG9qdpXjRhpjRhg3h/jPVe1Nx3PEW2WTDmi7hvqJw9c5lHqexfGq4+u71RgsYaZ7P4cOHW/H3P5SSdVOX6h/ftWtX23TTTd3vPY3PAqAsUAVXS0tLkzWbcLt/n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" width="672" /></p>
<pre class="r"><code>annotate_figure(figure, 
                bottom = text_grob(&quot;Average Marginal Effect&quot;, 
                                   color = &quot;black&quot;,
                                   hjust = 0.25, vjust = 0, size = 12))</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>#############

########################################################################################################
########################################################################################################
########################################################################################################

#Replication for additional analyses, including online tables and figures#

########################################################################################################
########################################################################################################
########################################################################################################


# ONLINE APPENDIX: Descriptive Statistics, CES Module, 2018/2020 year-by-year comparison
dfs_by_year &lt;- stby(data = desc.stats2, 
                    INDICES = desc.stats2$cces.year, 
                    FUN = descr, stats = &quot;common&quot;, transpose = TRUE)

###### ONLINE APPENDIX - T-TESTS FOR FAMILIARITY BIAS #####

# subsetting observations by states
# with and w/o partisan school boards
states_sb_p &lt;- subset(cces2.2, states_p_sb==1)
states_sb_np &lt;- subset(cces2.2, states_p_sb==0)

#t-test: proportion choosing np school board living in
# PARTISAN school board states vs. proportion choosing 
# np school board living in NONPARTISAN school board states
t.test(states_sb_p$RCO322_np,states_sb_np$RCO322_np) </code></pre>
<pre><code>## 
##  Welch Two Sample t-test
## 
## data:  states_sb_p$RCO322_np and states_sb_np$RCO322_np
## t = -1.2801, df = 185.22, p-value = 0.2021
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.13914585  0.02963509
## sample estimates:
## mean of x mean of y 
## 0.5263158 0.5810712</code></pre>
<pre class="r"><code># subsetting observations by states
# with and w/o partisan sheriffs
states_sher_p &lt;- subset(cces2.2, states_p_sheriff==1)
states_sher_np &lt;- subset(cces2.2, states_p_sheriff==0)

#t-test: proportion choosing np sheriff living in
# PARTISAN sheriff states vs. proportion choosing 
# np sheriffd living in NONPARTISAN sheriff states
t.test(states_sher_p$RCO323_np,states_sher_np$RCO323_np) </code></pre>
<pre><code>## 
##  Welch Two Sample t-test
## 
## data:  states_sher_p$RCO323_np and states_sher_np$RCO323_np
## t = 0.22578, df = 304.64, p-value = 0.8215
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.06334852  0.07976982
## sample estimates:
## mean of x mean of y 
## 0.5149371 0.5067265</code></pre>
<pre class="r"><code># subsetting observations by states
# with and w/o partisan D.A.
states_da_p &lt;- subset(cces2.2, states_p_da==1)
states_da_np &lt;- subset(cces2.2, states_p_da==0)

#t-test: proportion choosing np d.a. living in
# PARTISAN d.a. states vs. proportion choosing 
# np d.a. living in NONPARTISAN d.a. states
t.test(states_da_p$RCO324_np,states_da_np$RCO324_np) </code></pre>
<pre><code>## 
##  Welch Two Sample t-test
## 
## data:  states_da_p$RCO324_np and states_da_np$RCO324_np
## t = -0.28396, df = 237.54, p-value = 0.7767
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.08823354  0.06600194
## sample estimates:
## mean of x mean of y 
## 0.4996369 0.5107527</code></pre>
<pre class="r"><code>##### ONLINE APPENDIX: Patterns of Response #####

#choosing PARTISAN for every office
mytable &lt;- table(cces2.2$partydefender)
margin.table(mytable, 1)</code></pre>
<pre><code>## 
##   0   1   2   3   4   5   6   7 
## 652 121 226  68 154  98  84 205</code></pre>
<pre class="r"><code>partydefender &lt;- subset(cces2.2, partydefender==7)
table(partydefender$pid)</code></pre>
<pre><code>## 
##   Democrat Republican 
##         94        111</code></pre>
<pre class="r"><code>#choosing NONPARTISAN for every office
mytable &lt;- table(cces2.2$reformer)
margin.table(mytable, 1)</code></pre>
<pre><code>## 
##   0   1   2   3   4   5   6   7 
## 439 125 122 137 122 185 135 343</code></pre>
<pre class="r"><code>reformer &lt;- subset(cces2.2, reformer==7)
table(reformer$pid)</code></pre>
<pre><code>## 
##   Democrat Republican 
##        217        126</code></pre>
<pre class="r"><code>#choosing NONPARTISAN for every local office
mytable &lt;- table(cces2.2$reformer_local)
margin.table(mytable, 1)</code></pre>
<pre><code>## 
##   0   1   2   3   4   5 
## 477 130 143 186 179 504</code></pre>
<pre class="r"><code>reformer_local &lt;- subset(cces2.2, reformer_local==5)
table(reformer_local$pid)</code></pre>
<pre><code>## 
##   Democrat Republican 
##        309        195</code></pre>
<pre class="r"><code>##### ONLINE APPENDIX: Number of Respondents by County Presidential Vote ####

mytable &lt;- table(cces2.2$pid,
                 cces2.2$Pres_Vote)
mytable</code></pre>
<pre><code>##             
##              Lean Democrat Lean Republican Solid Democrat Solid Republican
##   Democrat             328             238            256              123
##   Republican           190             214             92              216</code></pre>
<pre class="r"><code>##### ONLINE APPENDIX: Overall preference for local, regardless of party ####

overallSB &lt;- cces2.2
overallSB &lt;- summarySE(cces2.2, measurevar=&quot;RCO322_np&quot;, na.rm=TRUE)
overallSB$office &lt;- &quot;school board&quot;
overallSB$preference &lt;- &quot;nonpartisan&quot;
overallSB$proportion &lt;- overallSB$RCO322_np
overallSB$RCO322_np &lt;- NULL
overallSB$.id &lt;- NULL

overallSBp &lt;- summarySE(cces2.2, measurevar=&quot;RCO322_p&quot;, na.rm=TRUE)
overallSBp$office &lt;- &quot;school board&quot;
overallSBp$preference &lt;- &quot;partisan&quot;
overallSBp$proportion &lt;- overallSBp$RCO322_p
overallSBp$RCO322_p &lt;- NULL
overallSBp$.id &lt;- NULL

overallSBa &lt;- cces2.2
overallSBa &lt;- summarySE(cces2.2, measurevar=&quot;RCO322_a&quot;, na.rm=TRUE)
overallSBa$office &lt;- &quot;school board&quot;
overallSBa$preference &lt;- &quot;appointed&quot;
overallSBa$proportion &lt;- overallSBa$RCO322_a
overallSBa$RCO322_a &lt;- NULL
overallSBa$.id &lt;- NULL

overall &lt;- rbind(overallSB, overallSBp, overallSBa)
###
###

overallleo &lt;- cces2.2
overallleo &lt;- summarySE(cces2.2, measurevar=&quot;RCO321_np&quot;, na.rm=TRUE)
overallleo$office &lt;- &quot;local election official&quot;
overallleo$preference &lt;- &quot;nonpartisan&quot;
overallleo$proportion &lt;- overallleo$RCO321_np
overallleo$RCO321_np &lt;- NULL
overallleo$.id &lt;- NULL

overallleop &lt;- summarySE(cces2.2, measurevar=&quot;RCO321_p&quot;, na.rm=TRUE)
overallleop$office &lt;- &quot;local election official&quot;
overallleop$preference &lt;- &quot;partisan&quot;
overallleop$proportion &lt;- overallleop$RCO321_p
overallleop$RCO321_p &lt;- NULL
overallleop$.id &lt;- NULL

overallleoa &lt;- cces2.2
overallleoa &lt;- summarySE(cces2.2, measurevar=&quot;RCO321_a&quot;, na.rm=TRUE)
overallleoa$office &lt;- &quot;local election official&quot;
overallleoa$preference &lt;- &quot;appointed&quot;
overallleoa$proportion &lt;- overallleoa$RCO321_a
overallleoa$RCO321_a &lt;- NULL
overallleoa$.id &lt;- NULL

overall &lt;- rbind(overall, overallleo, overallleop, overallleoa)
###
###
overallsher &lt;- cces2.2
overallsher &lt;- summarySE(cces2.2, measurevar=&quot;RCO323_np&quot;, na.rm=TRUE)
overallsher$office &lt;- &quot;sheriff&quot;
overallsher$preference &lt;- &quot;nonpartisan&quot;
overallsher$proportion &lt;- overallsher$RCO323_np
overallsher$RCO323_np &lt;- NULL
overallsher$.id &lt;- NULL

overallsherp &lt;- summarySE(cces2.2, measurevar=&quot;RCO323_p&quot;, na.rm=TRUE)
overallsherp$office &lt;- &quot;sheriff&quot;
overallsherp$preference &lt;- &quot;partisan&quot;
overallsherp$proportion &lt;- overallsherp$RCO323_p
overallsherp$RCO323_p &lt;- NULL
overallsherp$.id &lt;- NULL

overallshera &lt;- cces2.2
overallshera &lt;- summarySE(cces2.2, measurevar=&quot;RCO323_a&quot;, na.rm=TRUE)
overallshera$office &lt;- &quot;sheriff&quot;
overallshera$preference &lt;- &quot;appointed&quot;
overallshera$proportion &lt;- overallshera$RCO323_a
overallshera$RCO323_a &lt;- NULL
overallshera$.id &lt;- NULL

overall &lt;- rbind(overall, overallsher, overallsherp, overallshera)
###
###
overallda &lt;- cces2.2
overallda &lt;- summarySE(cces2.2, measurevar=&quot;RCO324_np&quot;, na.rm=TRUE)
overallda$office &lt;- &quot;district attorney&quot;
overallda$preference &lt;- &quot;nonpartisan&quot;
overallda$proportion &lt;- overallda$RCO324_np
overallda$RCO324_np &lt;- NULL
overallda$.id &lt;- NULL

overalldap &lt;- summarySE(cces2.2, measurevar=&quot;RCO324_p&quot;, na.rm=TRUE)
overalldap$office &lt;- &quot;district attorney&quot;
overalldap$preference &lt;- &quot;partisan&quot;
overalldap$proportion &lt;- overalldap$RCO324_p
overalldap$RCO324_p &lt;- NULL
overalldap$.id &lt;- NULL

overalldaa &lt;- cces2.2
overalldaa &lt;- summarySE(cces2.2, measurevar=&quot;RCO324_a&quot;, na.rm=TRUE)
overalldaa$office &lt;- &quot;district attorney&quot;
overalldaa$preference &lt;- &quot;appointed&quot;
overalldaa$proportion &lt;- overalldaa$RCO324_a
overalldaa$RCO324_a &lt;- NULL
overalldaa$.id &lt;- NULL

overall &lt;- rbind(overall, overallda, overalldap, overalldaa)

###
overallhouse &lt;- cces2.2
overallhouse &lt;- summarySE(cces2.2, measurevar=&quot;RCO327_np&quot;, na.rm=TRUE)
overallhouse$office &lt;- &quot;u.s. house&quot;
overallhouse$preference &lt;- &quot;nonpartisan&quot;
overallhouse$proportion &lt;- overallhouse$RCO327_np
overallhouse$RCO327_np &lt;- NULL
overallhouse$.id &lt;- NULL

overallhousep &lt;- summarySE(cces2.2, measurevar=&quot;RCO327_p&quot;, na.rm=TRUE)
overallhousep$office &lt;- &quot;u.s. house&quot;
overallhousep$preference &lt;- &quot;partisan&quot;
overallhousep$proportion &lt;- overallhousep$RCO327_p
overallhousep$RCO327_p &lt;- NULL
overallhousep$.id &lt;- NULL

overallhousea &lt;- cces2.2
overallhousea &lt;- summarySE(cces2.2, measurevar=&quot;RCO327_a&quot;, na.rm=TRUE)
overallhousea$office &lt;- &quot;u.s. house&quot;
overallhousea$preference &lt;- &quot;appointed&quot;
overallhousea$proportion &lt;- overallhousea$RCO327_a
overallhousea$RCO327_a &lt;- NULL
overallhousea$.id &lt;- NULL

overall &lt;- rbind(overall, overallhouse, overallhousep, overallhousea)
##
##
##
overall$office &lt;- ordered(overall$office, 
                          levels = c(&quot;school board&quot;, &quot;sheriff&quot;,
                                     &quot;district attorney&quot;, 
                                     &quot;local election official&quot;,
                                     &quot;u.s. house&quot;))

overallPlot &lt;- ggplot(overall, aes(x=office, y=proportion, fill=preference)) + 
  scale_fill_manual(values=c(&quot;palegreen&quot;, &quot;purple&quot;, &quot;black&quot;),
                    name=&quot;Preference&quot;, # Legend label, use darker colors
                    breaks=c(&quot;nonpartisan&quot;, &quot;partisan&quot;, &quot;appointed&quot;),
                    labels=c(&quot;nonpartisan&quot;, &quot;partisan&quot;, &quot;appointed&quot;)) +
  geom_bar(position=position_dodge(), stat=&quot;identity&quot;,
           colour=&quot;black&quot;, # Use black outlines,
           size=.3) +      # Thinner lines
  geom_errorbar(aes(ymin=proportion-se, ymax=proportion+se),
                size=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9)) +
  xlab(&quot;Office&quot;) +
  ylab(&quot;Proportion of Respondents&quot;) +
  ylim(0,0.75) +
  ggtitle(&quot;&quot;) +
  theme_bw()

overallPlot</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<pre class="r"><code>### ONLINE APPENDIX: Inter-party differences, DV=Partisan Election ###

sbContrastP &lt;- cces2.2
sbContrastP &lt;- subset(sbContrastP, c(Pres_Vote == &quot;Solid Democrat&quot; | Pres_Vote == &quot;Solid Republican&quot;))

sbContrastP$pid &lt;- factor(sbContrastP$pid)
sbContrastPD &lt;- subset(sbContrastP, pid==&quot;Democrat&quot;)
sbContrastPR &lt;- subset(sbContrastP, pid==&quot;Republican&quot;)

sbContrastPD &lt;- summarySE(sbContrastPD, measurevar=&quot;RCO322_p&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
sbContrastPD$office &lt;- &quot;school board&quot;
sbContrastPD$party &lt;- &quot;Democrat&quot;
sbContrastPD$proportion &lt;- sbContrastPD$RCO322_p
sbContrastPD$RCO322_p &lt;- NULL


sbContrastPR &lt;- summarySE(sbContrastPR, measurevar=&quot;RCO322_p&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
sbContrastPR$office &lt;- &quot;school board&quot;
sbContrastPR$party &lt;- &quot;Republican&quot;
sbContrastPR$proportion &lt;- sbContrastPR$RCO322_p
sbContrastPR$RCO322_p &lt;- NULL

sbContrastPDR &lt;- rbind(sbContrastPD, sbContrastPR)
sbContrastPDR$Pres_Vote &lt;- ordered(sbContrastPDR$Pres_Vote, 
                                   levels = c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;))

pd &lt;- position_dodge(0.1)
sbCDP &lt;- ggplot(sbContrastPDR, aes(x=Pres_Vote, y=proportion, colour=party, group=party, legend=&quot;right&quot;)) + 
  geom_errorbar(aes(ymin=proportion-ci, ymax=proportion+ci), colour=&quot;black&quot;, width=.1, position=pd) +
  geom_line(position=pd) +
  geom_point(position=pd, size=3, shape=21, fill=&quot;white&quot;) + # 21 is filled circle
  xlab(&quot;&quot;) +
  ylab(&quot;&quot;) +
  scale_color_manual(values=c(&quot;Democrat&quot; = &quot;blue&quot;, 
                              &quot;Republican&quot; = &quot;red&quot;)) +
  scale_x_discrete(breaks=c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;),
                   labels=c(&quot;Solid\nDemocrat&quot;, &quot;Solid\nRepublican&quot;)) +
  ggtitle(&quot;School Board&quot;) +
  ylim(0,1) +                        
  theme_bw() 


sbCDP</code></pre>
<p><img 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NMr5QwePLjtgAMOmO+nFCi2ffrTn24rvcu1bemll66cX3pvZlspXOz0Vnk+b9zka1/7WuXee+21V6f3jQNHHXVU5dz4XnZcSuF95Xh8V0o9j9tKr1yonDZ9+vS2aNfyst9++1XOL4XwbaVZ78uH2n3+6Ec/qpxX/t9JdTntTrZBgAABAgQIECBAgAABAgQItKxADD+2NLHA2Wef3Vaa+GW+oKcc+JQ/I6j8+te/3lYaqr7Apy1NXFMpL8KrrpbTTjutcm5pKHzl1OoANELU0vDryrGOK6Uee5Uyor6lnoLtTin1+mwrDbuvnBNBYVdLqZdq25JLLlk5///9v//X7vS8y6tHAFput+58loabt11xxRXtnrF6I+/njUCy1Au54lvq6Vl9u/nWS++orZwbz1PqsdvunOoAtNTbs600UVa749UbpV6ebaXXN1TK6+q547rSxFiVc+PeAtBqTesECBAgQIAAAQIECBAgQKBvCBgCX0pFmnmJIcbxXsWLLrooexfo4osvXvNxYthwDD+OIeKHHXZYNgS91okPPvhgNtlMHCv1Qmw31L7W+TH0OSasufTSS7NJbmqds/3226c11lij1qFsX8zaHsO3y0vHWczLQ6vjeKlHZzrooIPKp9b8jNnlY2Kd8hLvi6xe8i6vuuxGrMd7YPfee+9syPirr746XxXyft54j2y8iiDKjXfALmiyqVVWWaVdnTqbvCpOWn/99VN8Hzpboi1jOH8sa621Vtptt906OzXb/53vfKfL4w4SIECAAAECBAgQIECAAAECrS9gFvgWaON4R2IEkfET7++Md4D+9a9/zSYdikli4r2M5SXCo1KvzVQa1p6dE+9arF4mTJhQ2YzZxEu9SyvbtVZiwpuYBbyrZaONNurqcPaOx7hPObyLd0VWL6UegZXNbbbZpt0kTJUDHVZ22GGHyp7HHnussh4reZfXrvCcNuI9r6WerjVLK/VizN51GZMaRTvH+zijXS+//PJsNvZ4P+uYMWMq19bjeeN7E+3aVdtGnUqvKsjqWKlMaSW+o50tXQXlcc1tt91WuXTzzTevvF+0srPDytixYzvssUmAAAECBAgQIECAAAECBAj0NQEBaIu1eOldkOkDH/hA9hMTIkVYduONN6bLLrssXXLJJZXw6a677kqld0xmPTerCUrvU6xsrrTSSpX1d7NSGo6+wMurZ6Uvdb5ud351gNexN2G7E6s2SsP4K1sxSVJMwrTccstl+/Iur3KjHFei923pXZcLLDEmeyq9piCdeuqp2bkR9h588MHpqquuqlxb7+eNOkRv0Jj0KMLYxx9/PEU9Yr1Wb8+O7VupaGklZrHvaqn+fsbkRwtaIlgPy1r1WNC1jhMgQIAAAQIECBAgQIAAAQKtIWAIfGu0Y6dPMWTIkLTLLruk3/72t1koVXonYuXc6DFYHY7FgUmTJlWOV/cirOzsxUrU4d0sjz76aOXy7gagEbpW92596qmnKmXkXV6l4AasRO/fcePGtRvy/+c//zndc889ldrU63mjx+73vve9bBb2LbfcMgvUS5MtpSuvvDLFqxTKoWMMme/uEq846Gqp/n52JwCNsrp7Xlf3dYwAAQIECBAgQIAAAQIECBBoXgEBaJO1XWlG8yxciqHP0bOzJ0tphvX0hz/8oXJJ9MSLdzhWL0OHDq1sRs++IiwR8pWXcqhW3u7sM4ZfV59b/Vx5l9dZHRbm/j333LPd7W6//fbKdj2ed+LEiWmTTTZJxx13XHrttdcq94qV0aNHp5122il7f2x8v+L9s9VL9fteq/fHenVP4I7HYnvgwIGV3d39flY/f+ViKwQIECBAgAABAgQIECBAgECfETAEvsma+tprr03lsCuG9kaPuM4mPqr1aDE0PN6dePfdd2eHq3tGxo7qdzDG5EpFWGJY9E033ZRV5dlnn+1WlWKSnup3TS6zzDKV6/Iur1JwA1c23XTTFL1ep0yZktXimWeeqdQm7+ctzQKfdt5552wipLhJhJJf/epXU2k29+ydoEsvvXTl3rHy+uuvt9vuagh8uxNrbJRfYxCHnnvuuRpnzL/r+eefn3+nPQQIECBAgAABAgQIECBAgECfERCANllTr7vuupUaRw/HeNfjPvvsU9nXnZXqEKnj8PTqADR6+XVn+cY3vpG9ZzHC1d133z0L4rpzXXfPqX4vZHcD0OrzFllkkVQdyuVdXnefo57nRShZPXlUTGBVXvJ+3gijH3744az4eOds9ETedttty7eb73Py5Mnt9lUH0+0OdGOjeoh8dwLQqVOnZhNGdaNopxAgQIAAAQIECBAgQIAAAQItKmAIfJM1bIRZ1b0ZL7zwwh49wZtvvplilvDysvbaa5dXs8/qADR6h8ZM410tMSnNz372s3TCCSekQw89tN0Q5a6u68mx6gAv6t5xyHWtsi644ILK7q233jqbab68I+/yyuU28nPChAnZTPDlOsQzl5e8n/fmm28uF53e//73dxl+xol33HFH5fxYidcT9Hb56Ec/Wrl0/PjxC/wuVH/XKxdaIUCAAAECBAgQIECAAAECBPqUgAC0yZo7hht//etfr9T66quvziaiqezoYmX27NnpO9/5TuXdmPFezN12263dFdHDLmaRjyWCqgg2u1picqXy8qEPfahHw/HL1y3o88Mf/nAqz0gfAe6JJ57Y5SUR3J577rmVc/baa6/KeqzkXV67whuwETPcx8zv5SUmf6qe7Crv561+p+eyyy5bvm3Nz1deeSV9//vfb3fs3fQAjWB3+eWXz8qLXq8//vGP25VdvTFv3rx07LHHVu+yToAAAQIECBAgQIAAAQIECPRBAQFoEzZ6hF3VQ4FjIproiRcTztTqHRnvXLz//vtThEenn3565Ym/8pWvpFGjRlW2yyunnHJKimHjsURPynPOOad8qN1nzDR+9NFHV/Z9/vOfr6znuRLD9KOXaXk57bTT0i9/+cvyZrvPp59+OntHajlkW2GFFdJ+++3X7py8y2tX+ELaiHAvnvWkk05K22yzTap+5+cxxxzTbjKhvJ93o402qjxlDH+PALbWEu8jjfD18ccfb3c4QuzeLjGB0g9+8IPK5ePGjUsXXXRRZbu8Eu0f/1BQHqpf3u+TAAECBAgQIECAAAECBAgQ6HsC3gHahG0+fPjwFJMhRfBVnvQmhkCXhwePGTMmxaQ4I0eOTI888kgWAnUMnaK35g9/+MOaTx/X7r///unss8/OJhI64IAD0nXXXZd22WWXtMEGG6To1RdDiyN8il6lscQ5HXta1iy8lzv32GOPNHbs2PTXv/41zZo1K335y1/OAt9dd901m408Qrg777wzq1P5XZgR4kaAGw4dl7zL61j+u92Oya06m708ws9ywNvxPp/61KdSvJO145Ln837iE5/IelbGLOzxfs/tt98+HX/88WnLLbdMgwYNSn/729/SLbfckv70pz9lwWyElsOGDUvRYzOWV199tWP1erQd380IPSN8jTrsu+++2ffx4x//eFpttdXSvffem84777x02223ZUF+OM6YMaNH93AyAQIECBAgQIAAAQIECBAg0EICpd6BliYVePDBB9tKIWhb6evY7Z9SGNVWCpDa3njjjS6fujTBUtsRRxzRFucvqPxSmNoW51cvpcmQKtedccYZ1Ydqro8ePbpy/n333VfznKjzgQceWDmvq3qVJj1qK4W2Ncsp78yrvO22265Sp4svvrhcfI8/S71zK+V09Wy1jpWGvbd97WtfayuFjJ3eN6/njRuUwu9u1bXUw7jtz3/+c1upN2bl/COPPLJdHUuzx1eOlXobtzvW2UY8Zynwr1xXy6T0uoi2Sy+9tK00cVjlvFIQ2lmR9hMgQIAAAQIECBAgQIAAAQItKmAIfCk5adZlvfXWy3q5RW+46AkZvew6W+K9kNE7MHrmxZD2xRdfvLNTs/3Ra+6nP/1p1uMyhjz3799/vvOjp2kp8Mt633XWW3G+i97Fjqjzr371qxTvPY2eqLXqFEP6owdkKURNO+64Y5d3y7u8Lm+W48HBgwdnPR3jXa1777139h7MiRMnpnh1QVffgTyf97DDDsva4T3veU/NJ4vvRrxv9rHHHksf+9jH2rVFfGeiF+u7WeI5//jHP6Z4HUL16yDKZcbET7///e+z73x5n08CBAgQIECAAAECBAgQIECgbwr0i2C3bz566z11DAcu9QrNhhjHMOO33347xSQ1yy23XIoQM4Kz3i6lHp7ZUPp4p2Kpd2Uq9apLEXLF8OZGLVGnGOL/0EMPZcFfBGHrr79+Ngy7N3XKu7ze1GFhXpPH80aQ+eyzz6aYeComR1p99dWzNhgxYsRCe5R4HUB875944oms7eNdt/EdtRAgQIAAAQIECBAgQIAAAQIEQkAA6ntAgAABAgQIECBAgAABAgQIECBAgEDLChgC37JN68EIECBAgAABAgQIECBAgAABAgQIEBCA+g4QIECAAAECBAgQIECAAAECBAgQINCyAgLQlm1aD0aAAAECBAgQIECAAAECBAgQIECAgADUd4AAAQIECBAgQIAAAQIECBAgQIAAgZYVEIC2bNN6MAIECBAgQIAAAQIECBAgQIAAAQIEBKC+AwQIECBAgAABAgQIECBAgAABAgQItKyAALRlm9aDESBAgAABAgQIECBAgAABAgQIECAgAPUdIECAAAECBAgQIECAAAECBAgQIECgZQUEoC3btB6MAAECBAgQIECAAAECBAgQIECAAAEBqO8AAQIECBAgQIAAAQIECBAgQIAAAQItKyAAbdmm9WAECBAgQIAAAQIECBAgQIAAAQIECAhAfQcIECBAgAABAgQIECBAgAABAgQIEGhZAQFoyzatByNAgAABAgQIECBAgAABAgQIECBAQADqO0CAAAECBAgQIECAAAECBAgQIECAQMsKCEBbtmk9GAECBAgQIECAAAECBAgQIECAAAECAlDfAQIECBAgQIAAAQIECBAgQIAAAQIEWlZAANqyTevBCBAgQIAAAQIECBAgQIAAAQIECBAQgPoOECBAgAABAgQIECBAgAABAgQIECDQsgIC0JZtWg9GgAABAgQIECBAgAABAgQIECBAgMAABM0jMHfu3PT00083T4XVlACBhgm0tbVV7t2vX7/KuhUCBAgQINCqAtV/9sUz+vOvVVvacxHIT2DRRRdNK620Un4FKokAgcIK9Cv9ReH//l9yYaupYiEwefLktPXWW8MgQIAAAQIECBAgQIAAAQIE3qXA6quvnq6++up3WYrLCRBoBgE9QJuhlTrU8dBDD03ve9/7Ouy1SYAAgf8TmDp1apo3b17W+2XJJZf8vwPWCBAgQIBAiwrMmTMnTZs2LXu6QYMGpcUXX7xFn9RjESCQh8D555+fnn322TyKUgYBAk0gIABtgkbqWMU11lgjbbHFFh132yZAgEBF4JVXXqkEoKNHj67st0KAAAECBFpVYPbs2em1117LHm/w4MFp5MiRrfqonosAgRwErrnmGgFoDo6KINAsAiZBapaWUk8CBAgQIECAAAECBAgQIECAAAECBHosIADtMZkLCBAgQIAAAQIECBAgQIAAAQIECBBoFgEBaLO0lHoSIECAAAECBAgQIECAAAECBAgQINBjAQFoj8lcQIAAAQIECBAgQIAAAQIECBAgQIBAswgIQJulpdSTAAECBAgQIECAAAECBAgQIECAAIEeCwhAe0zmAgIECBAgQIAAAQIECBAgQIAAAQIEmkVAANosLaWeBAgQIECAAAECBAgQIECAAAECBAj0WEAA2mMyFxAgQIAAAQIECBAgQIAAAQIECBAg0CwCAtBmaSn1JECAAAECBAgQIECAAAECBAgQIECgxwIC0B6TuYAAAQIECBAgQIAAAQIECBAgQIAAgWYREIA2S0upJwECBAgQIECAAAECBAgQIECAAAECPRYQgPaYzAUECBAgQIAAAQIECBAgQIAAAQIECDSLgAC0WVpKPQkQIECAAAECBAgQIECAAAECBAgQ6LGAALTHZC4gQIAAAQIECBAgQIAAAQIECBAgQKBZBASgzdJS6kmAAAECBAgQIECAAAECBAgQIECAQI8FBKA9JnMBAQIECBAgQIAAAQIECBAgQIAAAQLNIiAAbZaWUk8CBAgQIECAAAECBAgQIECAAAECBHosIADtMZkLCBAgQIAAAQIECBAgQIAAAQIECBBoFgEBaLO0lHoSIECAAAECBAgQIECAAAECBAgQINBjAQFoj8lcQIAAAQIECBAgQIAAAQIECBAgQIBAswgIQJulpdSTAAECBAgQIECAAAECBAgQIECAAIEeCwhAe0zmAgIECBAgQIAAAQIECBAgQIAAAQIEmkVAANosLaWeBAgQIECAAAECBAgQIECAAAECBAj0WEAA2mMyFxAgQIAAAQIECBAgQIAAAQIECBAg0CwCAtBmaSn1JECAAAECBAgQIECAAAECBAgQIECgxwIDenxFE13w9ttvpyOPPDKNHj06HX300b2q+aOPPpouv/zy9Nxzz6Vhw4alDTbYIO2www5ptdVW67S83lzTaWEOECBAgAABAgQIECBAgAABAgQIECDQa4GWDUDb2trScccdlx544IE0YEDvHvOKK65I48aNy3AXW2yxNGvWrPTPf/4zXXbZZenEE09Mm2666XzwvblmvkLsIECAAAECBAgQIECAAAECBAgQIEAgF4GWHAI/Y8aMdNJJJ6UJEyb0GimC01NPPTUNGjQoHX/88enqq69O1157bTrssMNSlB89S19++eV25ffmmnYF2CBAgAABAgQIECBAgAABAgQIECBAIFeBlgtA77777vS5z30u/eUvf0mLLNL7xzv//PNT9CL97Gc/m7bddtvUr1+/NHDgwLTXXnulPffcM82ePTv94Q9/aNcYvbmmXQE2CBAgQIAAAQIECBAgQIAAAQIECBDIVaD3CWGu1cinsBtuuCEdfvjhWc/MLbbYIh1xxBG9KjjeHXrXXXdl1+60007zlVHe9+c//znNmTMnO96ba+Yr2A4CBAgQIECAAAECBAgQIECAAAECBHIVaKkAdMqUKWn55ZdP3/rWt9KPf/zjNHLkyF5hPfLII1nvzzFjxmTldSxknXXWSYsvvniaNm1aev7557PDvbmmY7m2CRAgQIAAAQIECBAgQIAAAQIECBDIV6B3swPlW4fcShs7dmzaY489ej3pUbkiL774YrY6YsSI8q75PuPYm2++mSZOnJjNCN+ba+YrtLTjlltuSeeee26tQ2nw4MHZ/unTp6fJkyfXPMdOAgQIhMC8efMyiHiVh98XvhMECBAg0BcEyn/2xbPG5KX+/OsLre4ZCfRe4J133qn8nbn3pbiSAIFmEWipAHTUqFG5uL/11ltZOV0FoEsssUR2Tvnc8mdPrqlV2VdeeSXdcccdtQ5VerTGsPv4S52FAAEC3RHw+6I7Ss4hQIAAgVYSiDDUn3+t1KKehUD+AvF7IjoLWAgQ6BsCLTUEPq8mi/d5xhLD3DtbFltssexQ/KtRLL25JrvQfwgQIECAAAECBAgQIECAAAECBAgQqJtAS/UAzUtp2LBhWVFd/avxzJkzs3MWXXTR7LM319Sq7y677JI++MEP1jqUvXM0jkfv02WWWabmOXYSIEAgBF577bVsSE+/fv3S0ksvDYUAAQIECLS8wOzZs9PUqVOz54y/ow8fPrzln9kDEiDQe4EhQ4ak/v37974AVxIg0FQCAtAazVUeSv/GG2/UOPqfXfH+z1jKwWdvrvlPSe3/G7+E46fWMmDAf5prkUUW8Yu6FpB9BAjUFPAXu5osdhIgQIBAiwlUvwM0/gHQn38t1sAeh0DOAvF7wkKAQN8RMAS+RluXw8xyyFnjlFQOR8szzffmmlrl2keAAAECBAgQIECAAAECBAgQIECAQH4CAtAaluXh5THDewyl6bhMmzYtTZkyJUVPzDXXXDM73JtrOpZrmwABAgQIECBAgAABAgQIECBAgACBfAUEoDU8l19++bTOOuuk6dOnpzvvvHO+M8aPH5/mzp2bnTN06NDseG+uma9gOwgQIECAAAECBAgQIECAAAECBAgQyFWgzwegEyZMSDfccEN65pln2sF+5jOfybbPPffcVD0UftKkSeniiy/Oju21117v+pp2BdggQIAAAQIECBAgQIAAAQIECBAgQCBXgT4/CdK4cePSSy+9lA466KC06qqrVnBjJvZ11103PfLII+nAAw9M22+/fZozZ0668cYb0+TJk9M222yTdthhh8r5sdKba9oVYIMAAQIECBAgQIAAAQIECBAgQIAAgVwF+nwA2plmzBp52mmnpZNPPjldf/316cILL8xOjf177rln+tKXvpS9A7T6+t5cU329dQIECBAgQIAAAQIECBAgQIAAAQIE8hXo11Za8i2y9UqLnp9PPfVUCqoxY8akYcOGLfAhe3PNggqNnqdbb711OuWUU9JHPvKRBZ3uOAECfVjglVdeSfPmzUv9+vVLo0eP7sMSHp0AAQIE+opATF762muvZY87ePDgNHLkyL7y6J6TAIFeCBxzzDHprrvuSldffXUvrnYJAQLNJqAHaDdabMCAAWnttdfuxpn/d0pvrvm/q60RIECAAAECBAgQIECAAAECBAgQIJCHQJ+fBCkPRGUQIECAAAECBAgQIECAAAECBAgQIFBMAQFoMdtFrQgQIECAAAECBAgQIECAAAECBAgQyEFAAJoDoiIIECBAgAABAgQIECBAgAABAgQIECimgAC0mO2iVgQIECBAgAABAgQIECBAgAABAgQI5CAgAM0BUREECBAgQIAAAQIECBAgQIAAAQIECBRTQABazHZRKwIECBAgQIAAAQIECBAgQIAAAQIEchAQgOaAqAgCBAgQIECAAAECBAgQIECAAAECBIopIAAtZruoFQECBAgQIECAAAECBAgQIECAAAECOQgIQHNAVAQBAgQIECBAgAABAgQIECBAgAABAsUUEIAWs13UigABAgQIECBAgAABAgQIECBAgACBHAQEoDkgKoIAAQIECBAgQIAAAQIECBAgQIAAgWIKCECL2S5qRYAAAQIECBAgQIAAAQIECBAgQIBADgIC0BwQFUGAAAECBAgQIECAAAECBAgQIECAQDEFBKDFbBe1IkCAAAECBAgQIECAAAECBAgQIEAgBwEBaA6IiiBAgAABAgQIECBAgAABAgQIECBAoJgCAtBitotaESBAgAABAgQIECBAgAABAgQIECCQg4AANAdERRAgQIAAAQIECBAgQIAAAQIECBAgUEwBAWgx20WtCBAgQIAAAQIECBAgQIAAAQIECBDIQUAAmgOiIggQIECAAAECBAgQIECAAAECBAgQKKaAALSY7aJWBAgQIECAAAECBAgQIECAAAECBAjkICAAzQFREQQIECBAgAABAgQIECBAgAABAgQIFFNAAFrMdlErAgQIECBAgAABAgQIECBAgAABAgRyEBCA5oCoCAIECBAgQIAAAQIECBAgQIAAAQIEiikgAC1mu6gVAQIECBAgQIAAAQIECBAgQIAAAQI5CAhAc0BUBAECBAgQIECAAAECBAgQIECAAAECxRQQgBazXdSKAAECBAgQIECAAAECBAgQIECAAIEcBASgOSAqggABAgQIECBAgAABAgQIECBAgACBYgoIQIvZLmpFgAABAgQIECBAgAABAgQIECBAgEAOAgLQHBAVQYAAAQIECBAgQIAAAQIECBAgQIBAMQUEoMVsF7UiQIAAAQIECBAgQIAAAQIECBAgQCAHAQFoDoiKIECAAAECBAgQIECAAAECBAgQIECgmAIC0GK2i1oRIECAAAECBAgQIECAAAECBAgQIJCDgAA0B0RFECBAgAABAgQIECBAgAABAgQIECBQTAEBaDHbRa0IECBAgAABAgQIECBAgAABAgQIEMhBQACaA6IiCBAgQIAAAQIECBAgQIAAAQIECBAopoAAtJjtolYECBAgQIAAAQIECBAgQIAAAQIECOQgIADNAVERBAgQIECAAAECBAgQIECAAAECBAgUU0AAWsx2USsCBAgQIECAAAECBAgQIECAAAECBHIQEIDmgKgIAgQIECBAgAABAgQIECBAgAABAgSKKSAALWa7qBUBAgQIECBAgAABAgQIECBAgAABAjkICEBzQFQEAQIECBAgQIAAAQIECBAgQIAAAQLFFBCAFrNd1IoAAQIECBAgQIAAAQIECBAgQIAAgRwEBKA5ICqCAAECBAgQIECAAAECBAgQIECAAIFiCghAi9kuakWAAAECBAgQIECAAAECBAgQIECAQA4CAtAcEBVBgAABAgQIECBAgAABAgQIECBAgEAxBQSgxWwXtSJAgAABAgQIECBAgAABAgQIECBAIAcBAWgOiIogQIAAAQIECBAgQIAAAQIECBAgQKCYAgLQYraLWhEgQIAAAQIECBAgQIAAAQIECBAgkIOAADQHREUQIECAAAECBAgQIECAAAECBAgQIFBMAQFoMdtFrQgQIECAAAECBAgQIECAAAECBAgQyEFAAJoDoiIIECBAgAABAgQIECBAgAABAgQIECimgAC0mO2iVgQIECBAgAABAgQIECBAgAABAgQI5CAgAM0BUREECBAgQIAAAQIECBAgQIAAAQIECBRTQABazHZRKwIECBAgQIAAAQIECBAgQIAAAQIEchAQgOaAqAgCBAgQIECAAAECBAgQIECAAAECBIopIAAtZruoFQECBAgQIECAAAECBAgQIECAAAECOQgIQHNAVAQBAgQIECBAgAABAgQIECBAgAABAsUUEIAWs13UigABAgQIECBAgAABAgQIECBAgACBHAQEoDkgKoIAAQIECBAgQIAAAQIECBAgQIAAgWIKCECL2S5qRYAAAQIECBAgQIAAAQIECBAgQIBADgIC0BwQFUGAAAECBAgQIECAAAECBAgQIECAQDEFBKDFbBe1IkCAAAECBAgQIECAAAECBAgQIEAgBwEBaA6IiiBAgAABAgQIECBAgAABAgQIECBAoJgCAtBitotaESBAgAABAgQIECBAgAABAgQIECCQg4AANAdERRAgQIAAAQIECBAgQIAAAQIECBAgUEwBAWgx20WtCBAgQIAAAQIECBAgQIAAAQIECBDIQUAAmgOiIggQIECAAAECBAgQIECAAAECBAgQKKaAALSY7aJWBAgQIECAAAECBAgQIECAAAECBAjkICAAzQFREQQIECBAgAABAgQIECBAgAABAgQIFFNAAFrMdlErAgQIECBAgAABAgQIECBAgAABAgRyEBCA5oCoCAIECBAgQIAAAQIECBAgQIAAAQIEiikgAC1mu6gVAQIECBAgQIAAAQIECBAgQIAAAQI5CAhAc0BUBAECBAgQIECAAAECBAgQIECAAAECxRQQgBazXdSKAAECBAgQIECAAAECBAgQIECAAIEcBASgOSAqggABAgQIECBAgAABAgQIECBAgACBYgoIQIvZLmpFgAABAgQIECBAgAABAgQIECBAgEAOAgLQHBAVQYAAAQIECBAgQIAAAQIECBAgQIBAMQUGFLNaatWVwKxZs9Lbb7/d1SmOESDQxwXa2toygfj0+6KPfxk8PgECBPqIwNy5cytPGuv+/KtwWCFAoIbAnDlzUvnvzDUO20WAQIsJCECbqEHLv5zjL3PTpk1ropqrKgECjRTw+6KR+u5NgAABAo0QmD17tr8vNwLePQk0kUB0LJo3b14T1VhVCRB4NwIC0Hejt5Cv7devX3bHESNGpNGjRy/ku7sdAQLNJDBp0qTsL3Txe2PZZZdtpqqrKwECBAgQ6JVAhJ6TJ0/Orh08eHCKvzNbCBAg0JnAkCFDUv/+/Ts7bD8BAi0mIABt0gYth6FNWn3VJkBgIQr4fbEQsd2KAAECBBom0PHPu47bDauYGxMgUEgBvyMK2SwqRaBuAiZBqhutggkQIECAAAECBAgQIECAAAECBAgQaLSAALTRLeD+BAgQIECAAAECBAgQIECAAAECBAjUTUAAWjdaBRMgQIAAAQIECBAgQIAAAQIECBAg0GgBAWijW8D9CRAgQIAAAQIECBAgQIAAAQIECBCom4AAtG60CiZAgAABAgQIECBAgAABAgQIECBAoNECAtBGt4D7EyBAgAABAgQIECBAgAABAgQIECBQNwEBaN1oFUyAAAECBAgQIECAAAECBAgQIECAQKMFBKCNbgH3J0CAAAECBAgQIECAAAECBAgQIECgbgIC0LrRKpgAAQIECBAgQIAAAQIECBAgQIAAgUYLCEAb3QLuT4AAAQIECBAgQIAAAQIECBAgQIBA3QQEoHWjVTABAgQIECBAgAABAgQIECBAgAABAo0WEIA2ugXcnwABAgQIECBAgAABAgQIECBAgACBugkIQOtGq2ACBAgQIECAAAECBAgQIECAAAECBBotIABtdAu4PwECBAgQIECAAAECBAgQIECAAAECdRMQgNaNVsEECBAgQIAAAQIECBAgQIAAAQIECDRaQADa6BZwfwIECBAgQIAAAQIECBAgQIAAAQIE6iYgAK0brYIJECBAgAABAgQIECBAgAABAgQIEGi0gAC00S3g/gQIECBAgAABAgQIECBAgAABAgQI1E1AAFo3WgUTIECAAAECBAgQIECAAAECBAgQINBoAQFoo1vA/QkQIECAAAECBAgQIECAAAECBAgQqJuAALRutAomQIAAAQIECBAgQIAAAQIECBAgQKDRAgLQRreA+xMgQIAAAQIECBAgQIAAAQIECBAgUDcBAWjdaBVMgAABAgQIECBAgAABAgQIECBAgECjBQSgjW4B9ydAgAABAgQIECBAgAABAgQIECBAoG4CAtC60SqYAAECBAgQIECAAAECBAgQIECAAIFGCwhAG90C7k+AAAECBAgQIECAAAECBAgQIECAQN0EBKB1o1UwAQIECBAgQIAAAQIECBAgQIAAAQKNFhCANroF3J8AAQIECBAgQIAAAQIECBAgQIAAgboJCEDrRqtgAgQIECBAgAABAgQIECBAgAABAgQaLSAAbXQLuD8BAgQIECBAgAABAgQIECBAgAABAnUTEIDWjVbBBAgQIECAAAECBAgQIECAAAECBAg0WkAA2ugWcH8CBAgQIECAAAECBAgQIECAAAECBOomIACtG62CCRAgQIAAAQIECBAgQIAAAQIECBBotIAAtNEt4P4ECBAgQIAAAQIECBAgQIAAAQIECNRNQABaN1oFEyBAgAABAgQIECBAgAABAgQIECDQaAEBaKNbwP0JECBAgAABAgQIECBAgAABAgQIEKibgAC0brQKJkCAAAECBAgQIECAAAECBAgQIECg0QIC0Ea3gPsTIECAAAECBAgQIECAAAECBAgQIFA3AQFo3WgVTIAAAQIECBAgQIAAAQIECBAgQIBAowUEoI1uAfcnQIAAAQIECBAgQIAAAQIECBAgQKBuAgLQutEqmAABAgQIECBAgAABAgQIECBAgACBRgsIQBvdAu5PgAABAgQIECBAgAABAgQIECBAgEDdBASgdaNVMAECBAgQIECAAAECBAgQIECAAAECjRYQgDa6BdyfAAECBAgQIECAAAECBAgQIECAAIG6CQhA60arYAIECBAgQIAAAQIECBAgQIAAAQIEGi0gAG10C7g/AQIECBAgQIAAAQIECBAgQIAAAQJ1ExCA1o1WwQQIECBAgAABAgQIECBAgAABAgQINFpAANroFnB/AgQIECBAgAABAgQIECBAgAABAgTqJiAArRutggkQIECAAAECBAgQIECAAAECBAgQaLSAALTRLeD+BAgQIECAAAECBAgQIECAAAECBAjUTUAAWjdaBRMgQIAAAQIECBAgQIAAAQIECBAg0GgBAWijW8D9CRAgQIAAAQIECBAgQIAAAQIECBCom4AAtG60CiZAgAABAgQIECBAgAABAgQIECBAoNECAtBGt4D7EyBAgAABAgQIECBAgAABAgQIECBQNwEBaN1oFUyAAAECBAgQIECAAAECBAgQIECAQKMFBKCNbgH3J0CAAAECBAgQIECAAAECBAgQIECgbgIC0LrRKpgAAQIECBAgQIAAAQIECBAgQIAAgUYLCEAb3QLuT4AAAQIECBAgQIAAAQIECBAgQIBA3QQEoHWjVTABAgQIECBAgAABAgQIECBAgAABAo0WEIA2ugXcnwABAgQIECBAgAABAgQIECBAgACBugkIQOtGq2ACBAgQIECAAAECBAgQIECAAAECBBotIABtdAu4PwECBAgQIECAAAECBAgQIECAAAECdRMQgNaNVsEECBAgQIAAAQIECBAgQIAAAQIECDRaQADa6BZwfwIECBAgQIAAAQIECBAgQIAAAQIE6iYgAK0brYIJECBAgAABAgQIECBAgAABAgQIEGi0gAC00S3g/gQIECBAgAABAgQIECBAgAABAgQI1E1AAFo3WgUTIECAAAECBAgQIECAAAECBAgQINBoAQFoo1vA/QkQIECAAAECBAgQIECAAAECBAgQqJuAALRutAomQIAAAQIECBAgQIAAAQIECBAgQKDRAgLQRreA+xMgQIAAAQIECBAgQIAAAQIECBAgUDcBAWjdaBVMgAABAgQIECBAgAABAgQIECBAgECjBQSgjW4B9ydAgAABAgQIECBAgAABAgQIECBAoG4CAtC60SqYAAECBAgQIECAAAECBAgQIECAAIFGCwhAG90C7k+AAAECBAgQIECAAAECBAgQIECAQN0EBKB1o1UwAQIECBAgQIAAAQIECBAgQIAAAQKNFhCANroF3J8AAQIECBAgQIAAAQIECBAgQIAAgboJCEDrRqtgAgQIECBAgAABAgQIECBAgAABAgQaLSAAbXQLuD8BAgQIECBAgAABAgQIECBAgAABAnUTEIDWjVbBBAgQIECAAAECBAgQIECAAAECBAg0WmBAoyuQ9/1nzpyZrrjiinT33XenqVOnpjXXXDNtvPHGaeedd079+/fv1u0efPDBdP3113fr3K233jptueWWlXPHjx+f7r///sp2x5VRo0alfffdt+Nu2wQIECBAgAABAgQIECBAgAABAgQI1EGgpQLQ119/PR188MFp4sSJGdWSSy6Zrr322uzn9ttvT9/73vfSoEGDFsj47LPPpt///vcLPC9OWGqppdoFoH/84x/TPffc0+m1q622mgC0Ux0HCBAgQIAAAQIECBAgQIAAAQIECOQr0FIB6Pe///0s/Nxiiy3Sd7/73TR8+PD04osvpv/5n/9Jt956azr11FPTkUceuUDBDTfcMH3zm9/s9Lzo4XndddeloUOHpu22267deU888US2fdhhh6VFF1203bHYWHzxxefbZwcBAgQIECBAgAABAgQIECBAgAABAvURaJkA9OGHH0533XVXGjJkSPrBD36QBg8enImtsMIK6Wc/+1nafffd0zXXXJO+9KUvLTCEXGmllVL81FomT56czj777OxQhKwrr7xy5bRJkyalN954I+sVutdee1X2WyFAgAABAgQIECBAgAABAgQIECBAoDECLTMJ0s0335wJfvCDH6yEn2XSGAr/vve9L82aNSsLQcv7e/MZvUynTJmSdtttt/T+97+/XRHl3p9rr712u/02CBAgQIAAAQIECBAgQIAAAQIECBBojEDLBKAPPfRQJhjD32stEYDG0tUERbWuq953ww03ZO/3jED1v/7rv6oPZesdA9A5c+ZkYel8J9pBgAABAgQIECBAgAABAgQIECBAgMBCEWiZIfDxrs9YRowYUROuvL88QVLNk7rYOWPGjPTzn/88OyMmWlpsscXmO7scgEbwefjhh6d77703zZ07Nxty/973vjfFe0Fj0qSulsceeyxNmDCh5ilRVizvvPNOmj59es1z7CRAgEAItLW1ZRDx6feF7wQBAgQI9AWB8t+V41nj7+P+/OsLre4ZCfReYPbs2ZW/M/e+FFcSINAsAi0TgL711luZeTno7NgASyyxRLarfF7H4wvajt6f8f7PCDDHjh1b8/Qnn3wy2//b3/429e/fP62zzjrZX76efvrpdNNNN6W77747jRs3Lq2xxho1r4+d//rXv9JJJ51U8/jIkSOz/RGAvvnmmzXPsZMAAQIdBfy+6ChimwABAgRaXSACUH/+tXorez4C704gAtB58+a9u0JcTYBA0wi0RAAav7QiFIyls1nWyz02Z86c2avG+dOf/pRd94lPfCINGDA/WwSr//73v7Nzdtppp/SNb3wjm5ApdsT+Y489NsVETSeccEI666yzapaRXew/BAgQIECAAAECBAgQIECAAAECBAjkJjB/kpdb0QuvoEUWWSQLG2OYemcBZ3n/oEGDelyxGNoeQ9OjV+euu+5a8/qYff6iiy5Kr732Wtp4441Tv379Kuctv/zy6bjjjkv77rtvirJitvqtt966crx6ZfPNN08x0VKtJf4lO4LUoUOHpuHDh9c6xT4CBAhkAm+88UZlSI/fF74UBAgQINAXBGIIfHnY+8CBA7O/M/eF5/aMBAj0TiCygcgSLAQI9A2BlghAo6lGjRqV4v2enQ11Ke8fNmxYj1v2qquuyq6JGeY7e4dn/OIcM2ZM9lPrBssuu2zaYIMNsmHwMSS+swB0tdVWS/FTa4kh+BGAxi/qCEEtBAgQ6EwgfufF+z/jH2P8vuhMyX4CBAgQaCWBGM5aDkCj44I//1qpdT0LgfwFYmRndcel/O+gRAIEiiTQMv/cEQFoLOWgsyNy9IaKpfwezY7HO9uOofXXX399dnj33Xfv7LRu7V9mmWWy8yLItBAgQIAAAQIECBAgQIAAAQIECBAgUH+BlglAy+Fi9K6stZT3r7vuurUOd7ovhqvH+z2XW265tNFGG3V6XgyRP//889Pll1/e6TmTJk3Kjq244oqdnuMAAQIECBAgQIAAAQIECBAgQIAAAQL5CbRMAFqemf3GG2+cTycmSYpZ2GOJ93P2ZHnwwQez09dcc80uL5s6dWr69a9/nU477bT03HPPzXfulClT0kMPPZTtX2+99eY7bgcBAgQIECBAgAABAgQIECBAgAABAvkLtEwAuuWWW6ZVVlklm2TommuuaSd14YUXphh2vvLKK6ctttii3bEJEyakG264IT3zzDPt9pc3Yub2WFZdddXyrpqfEayOGDEie+feeeedl2LCovISw+hPOumkFJM0bbPNNmmdddYpH/JJgAABAgQIECBAgAABAgQIECBAgEAdBVpmEqR4efFBBx2Ujj766HTCCSekO+64I0WvzQceeCBbj5kgjzrqqPlecjxu3Lj00ksvZdfWCjlffPHFjL+ziYnKbTN48OD0ve99Lx1xxBEpeqHec889accdd8xmjr/11lvTCy+8kIWohx9+ePkSnwQIECBAgAABAgQIECBAgAABAgQI1FmgZQLQcNp2223TySefnAWg48ePT/ETS/QMjeBxww03zLa7+58YOh9D22OpFY52LGfzzTdPZ555ZopQ9ZFHHkmXXnppdsqQIUPSTjvtlIWjZqPsqGabAAECBAgQIECAAAECBAgQIECAQP0EWioADaZNNtkkm4gohrxPnDgxxeRIo0ePTossUnu0/2WXXdapblxz8803d3q81oF4v+dZZ52Vpk2blt1/8cUXT2PGjOn0/rXKsI8AAQIECBAgQIAAAQIECBAgQIAAgXwEWi4ALbMstdRSKX4atQwfPjzFj4UAAQIECBAgQIAAAQIECBAgQIAAgcYJ1O4W2bj6uDMBAgQIECBAgAABAgQIECBAgAABAgRyExCA5kapIAIECBAgQIAAAQIECBAgQIAAAQIEiiYgAC1ai6gPAQIECBAgQIAAAQIECBAgQIAAAQK5CQhAc6NUEAECBAgQIECAAAECBAgQIECAAAECRRMQgBatRdSHAAECBAgQIECAAAECBAgQIECAAIHcBASguVEqiAABAgQIECBAgAABAgQIECBAgACBogkIQIvWIupDgAABAgQIECBAgAABAgQIECBAgEBuAgLQ3CgVRIAAAQIECBAgQIAAAQIECBAgQIBA0QQEoEVrEfUhQIAAAQIECBAgQIAAAQIECBAgQCA3AQFobpQKIkCAAAECBAgQIECAAAECBAgQIECgaAIC0KK1iPoQIECAAAECBAgQIECAAAECBAgQIJCbgAA0N0oFESBAgAABAgQIECBAgAABAgQIECBQNAEBaNFaRH0IECBAgAABAgQIECBAgAABAgQIEMhNQACaG6WCCBAgQIAAAQIECBAgQIAAAQIECBAomoAAtGgtoj4ECBAgQIAAAQIECBAgQIAAAQIECOQmIADNjVJBBAgQIECAAAECBAgQIECAAAECBAgUTUAAWrQWUR8CBAgQIECAAAECBAgQIECAAAECBHITEIDmRqkgAgQIECBAgAABAgQIEBv6jkEAAEAASURBVCBAgAABAgSKJiAALVqLqA8BAgQIECBAgAABAgQIECBAgAABArkJCEBzo1QQAQIECBAgQIAAAQIECBAgQIAAAQJFExCAFq1F1IcAAQIECBAgQIAAAQIECBAgQIAAgdwEBKC5USqIAAECBAgQIECAAAECBAgQIECAAIGiCQhAi9Yi6kOAAAECBAgQIECAAAECBAgQIECAQG4CAtDcKBVEgAABAgQIECBAgAABAgQIECBAgEDRBASgRWsR9SFAgAABAgQIECBAgAABAgQIECBAIDcBAWhulAoiQIAAAQIECBAgQIAAAQIECBAgQKBoAgLQorWI+hAgQIAAAQIECBAgQIAAAQIECBAgkJuAADQ3SgURIECAAAECBAgQIECAAAECBAgQIFA0AQFo0VpEfQgQIECAAAECBAgQIECAAAECBAgQyE1AAJobpYIIECBAgAABAgQIECBAgAABAgQIECiagAC0aC2iPgQIECBAgAABAgQIECBAgAABAgQI5CYgAM2NUkEECBAgQIAAAQIECBAgQIAAAQIECBRNQABatBZRHwIECBAgQIAAAQIECBAgQIAAAQIEchMQgOZGqSACBAgQIECAAAECBAgQIECAAAECBIomIAAtWouoDwECBAgQIECAAAECBAgQIECAAAECuQkIQHOjVBABAgQIECBAgAABAgQIECBAgAABAkUTEIAWrUXUhwABAgQIECBAgAABAgQIECBAgACB3AQEoLlRKogAAQIECBAgQIAAAQIECBAgQIAAgaIJCECL1iLqQ4AAAQIECBAgQIAAAQIECBAgQIBAbgIC0NwoFUSAAAECBAgQIECAAAECBAgQIECAQNEEBKBFaxH1IUCAAAECBAgQIECAAAECBAgQIEAgNwEBaG6UCiJAgAABAgQIECBAgAABAgQIECBAoGgCAtCitYj6ECBAgAABAgQIECBAgAABAgQIECCQm4AANDdKBREgQIAAAQIECBAgQIAAAQIECBAgUDQBAWjRWkR9CBAgQIAAAQIECBAgQIAAAQIECBDITUAAmhulgggQIECAAAECBAgQIECAAAECBAgQKJqAALRoLaI+BAgQIECAAAECBAgQIECAAAECBAjkJiAAzY1SQQQIECBAgAABAgQIECBAgAABAgQIFE1AAFq0FlEfAgQIECBAgAABAgQIECBAgAABAgRyExCA5kapIAIECBAgQIAAAQIECBAgQIAAAQIEiiYgAC1ai6gPAQIECBAgQIAAAQIECBAgQIAAAQK5CQhAc6NUEAECBAgQIECAAAECBAgQIECAAAECRRMQgBatRdSHAAECBAgQIECAAAECBAgQIECAAIHcBASguVEqiAABAgQIECBAgAABAgQIECBAgACBogkIQIvWIupDgAABAgQIECBAgAABAgQIECBAgEBuAgLQ3CgVRIAAAQIECBAgQIAAAQIECBAgQIBA0QQEoEVrEfUhQIAAAQIECBAgQIAAAQIECBAgQCA3AQFobpQKIkCAAAECBAgQIECAAAECBAgQIECgaAIC0KK1iPoQIECAAAECBAgQIECAAAECBAgQIJCbgAA0N0oFESBAgAABAgQIECBAgAABAgQIECBQNAEBaNFaRH0IECBAgAABAgQIECBAgAABAgQIEMhNQACaG6WCCBAgQIAAAQIECBAgQIAAAQIECBAomoAAtGgtoj4ECBAgQIAAAQIECBAgQIAAAQIECOQmIADNjVJBBAgQIECAAAECBAgQIECAAAECBAgUTUAAWrQWUR8CBAgQIECAAAECBAgQIECAAAECBHITEIDmRqkgAgQIECBAgAABAgQIECBAgAABAgSKJiAALVqLqA8BAgQIECBAgAABAgQIECBAgAABArkJCEBzo1QQAQIECBAgQIAAAQIECBAgQIAAAQJFExCAFq1F1IcAAQIECBAgQIAAAQIECBAgQIAAgdwEBKC5USqIAAECBAgQIECAAAECBAgQIECAAIGiCQhAi9Yi6kOAAAECBAgQIECAAAECBAgQIECAQG4CAtDcKBVEgAABAgQIECBAgAABAgQIECBAgEDRBASgRWsR9SFAgAABAgQIECBAgAABAgQIECBAIDcBAWhulAoiQIAAAQIECBAgQIAAAQIECBAgQKBoAgLQorWI+hAgQIAAAQIECBAgQIAAAQIECBAgkJuAADQ3SgURIECAAAECBAgQIECAAAECBAgQIFA0AQFo0VpEfQgQIECAAAECBAgQIECAAAECBAgQyE1gQG4lKWihCcybNy/NnTt3od3PjQgQaG4Bvy+au/3UngABAgS6J1D9511bW5u/L3ePzVkE+qxA/J6wECDQdwQEoE3U1uVf0G+88UaaNGlSE9VcVQkQaJRA/N7w+6JR+u5LgAABAo0SmDlzpj//GoXvvgSaRGDGjBn+oaRJ2ko1CeQhIADNQ3EhldGvX7/sTkOHDk0jRoxYSHd1GwIEmlFg2rRpqfyPJn5fNGMLqjMBAgQI9FRgzpw5afr06dllAwcOTMOGDetpEc4nQKAPCQwaNCgtsoi3AvahJveofVxAANqEX4D4RT1kyJAmrLkqEyCwsASip3gEoPEPJ35fLCx19yFAgACBRgrMnj27EoD279/fn3+NbAz3JtAEAgMGDMj+rtwEVVVFAgRyEPDPHTkgKoIAAQIECBAgQIAAAQIECBAgQIAAgWIKCECL2S5qRYAAAQIECBAgQIAAAQIECBAgQIBADgIC0BwQFUGAAAECBAgQIECAAAECBAgQIECAQDEFBKDFbBe1IkCAAAECBAgQIECAAAECBAgQIEAgBwEBaA6IiiBAgAABAgQIECBAgAABAgQIECBAoJgCAtBitotaESBAgAABAgQIECBAgAABAgQIECCQg4AANAdERRAgQIAAAQIECBAgQIAAAQIECBAgUEwBAWgx20WtCBAgQIAAAQIECBAgQIAAAQIECBDIQUAAmgOiIggQIECAAAECBAgQIECAAAECBAgQKKaAALSY7aJWBAgQIECAAAECBAgQIECAAAECBAjkICAAzQFREQQIECBAgAABAgQIECBAgAABAgQIFFNAAFrMdlErAgQIECBAgAABAgQIECBAgAABAgRyEBCA5oCoCAIECBAgQIAAAQIECBAgQIAAAQIEiikgAC1mu6gVAQIECBAgQIAAAQIECBAgQIAAAQI5CAhAc0BUBAECBAgQIECAAAECBAgQIECAAAECxRQQgBazXdSKAAECBAgQIECAAAECBAgQIECAAIEcBASgOSAqggABAgQIECBAgAABAgQIECBAgACBYgoIQIvZLmpFgAABAgQIECBAgAABAgQIECBAgEAOAgLQHBAVQYAAAQIECBAgQIAAAQIECBAgQIBAMQUEoMVsF7UiQIAAAQIECBAgQIAAAQIECBAgQCAHAQFoDoiKIECAAAECBAgQIECAAAECBAgQIECgmAIC0GK2i1oRIECAAAECBAgQIECAAAECBAgQIJCDgAA0B0RFECBAgAABAgQIECBAgAABAgQIECBQTAEBaDHbRa0IECBAgAABAgQIECBAgAABAgQIEMhBQACaA6IiCBAgQIAAAQIECBAgQIAAAQIEagvcfvvttQ/YS2AhCQhAFxK02xAgQIAAAQIECBAgQIAAAQIE+pLAK6+8kvbbb780duzYvvTYnrWAAgLQAjaKKhEgQIAAAQIECBAgQIAAAQIEml3gmGOOSRdccEFqa2tr9kdR/yYXEIA2eQOqPgECBAgQIECAAAECBAgQIECAAAECnQsIQDu3cYQAAQIECBAgQIAAAQIECBAgQIAAgSYXGNDk9Vd9AgQIECBAgAABAgQIECBAgECfEXj++efTyy+/nBZbbLH0nve8Jxte/sgjj6QJEyakF154Ia266qrpwx/+cFphhRUWaDJjxoz0+OOPp0cffTQ99thjafDgwWnNNdfMfqLsRRaZv9/c7Nmz07333puVHecMHTo0xSRHN910U1puueXS7rvvnmbOnJnVZdKkSdl5MQT+rrvuytZHjx6dBgwYkB2PHWuvvXYaPnx4dqzWf95666300EMPZYdWX331tNRSS9U6zT4CXQoIQLvkcZAAAQIECBAgQIAAAQIECBAgUByBn/3sZ2ncuHFpiy22SFdffXX62Mc+lv7+97+3q+CgQYPSgQcemE4++eQU67WWX/7yl+nII49M06dPr3U4K//Xv/51Wn/99dsdnzx5cnYsdkboetppp6VLLrmkck6U+fWvfz0dd9xxlX2zZs2qXBPHdt1117TDDjtkx48++uh07LHHVs7tuHL22Wenr33ta9nuCGsFoB2FbHdHQADaHSXnECBAgAABAgQIECBAgAABAgQKJDBlypS07bbbZr0j11prrbTjjjumOXPmpOuvvz49/fTT6YwzzkhPPvlk+stf/pL1uCxXPXpjRmh6zTXXZLuWX375tOWWW6Yll1wy/fvf/856ar722mvpzjvvTFtttVWKHqcjR44sX97u81e/+lW78DMObrPNNmnllVdOm2++eXruuefSq6++mvr165c222yz7NoxY8ak7bbbLq2yyirp2WefTRdeeGGXAehvfvOb7LooN3qnWgj0RmCR3lzkGgIECBAgQIAAAQIECBAgQIAAgcYJPPHEE1n4ecABB6QHH3ww64l55plnZut77713VrEIQ2Nf9RLBZzn8POSQQ7IQ8sorr0wRZkZY+swzz6QvfOEL2SXRO/QXv/hF9eXt1s8777xs2HvcI4bFn3LKKVmv0i9+8YvpH//4R9pjjz2y86MXamzHzxFHHJEFop///OezY0899VS644472pVb3nj44YfTPffck22W61Q+5pNATwQEoD3Rci4BAgQIECBAgAABAgQIECBAoCACMQw+hqkPHDiwUqMhQ4ak3/72t1kvzNh5wgknVI7FSgyfj2XFFVfM1quvjf3xbtHTTz+90ms0eoJ2tUQI+uUvfzltvPHG2VD18tD2rq6JYxFoRs/QWC644ILss+N/4jliiWf61Kc+1fGwbQLdFhCAdpvKiQQIECBAgAABAgQIECBAgACB4ggcc8wxNSsToeY3v/nN7FhMmBSTI5WXE088MV122WXpnHPOSf379y/vbvc5bNiwLCCNnZ29IzSOxQRGMfS+N0sMgY+h8LFceumlKSZXql7mzZtXCUZ32223tMQSS1Qftk6gRwLeAdojLicTIECAAAECBAgQIECAAAECBBovEDO0jx07ttOKRO/Q8hLD06PHZyybbLJJ9lM+Vv05bdq0dP/996ebb745xWRHscR7RTtb3u07Offff/80fvz47F7XXntt2mWXXSq3iv3l4Nbw9wqLlV4KCEB7CecyAgQIECBAgAABAgQIECBAgECjBGLyoo7D16vrstJKK1U277vvvnbhYhyIgDN6gj7wwAPZu0Qfe+yx9Morr1Su6c7KGmus0Z3TOj0n3hF66KGHpjfeeCPr7VkdgJYnP4rgtqugt9PCHSBQJSAArcKwSoAAAQIECBAgQIAAAQIECBBoBoFll122y2pWDxmPmdyrl+985zvZ+z/ffvvt6t3ZevTqjFniL7roojRp0qT5jlfvGDVqVPVmj9eHDh2avdsz3mN61VVXZUFo1Putt95Kv/vd77Ly9ttvvxS9XS0E3o2Ab9C70XMtAQIECBAgQIAAAQIECBAgQKABAgvqrfniiy9WarX00ktX1r/97W+nH/7whynCzyWXXDLFMPSf//zn6bbbbkuvv/56evzxx9PJJ5+cIpxcGEvcP5YZM2ZUQs/f//73lXePGv6+MFqh9e+hB2jrt7EnJECAAAECBAgQIECAAAECBFpM4KWXXsrezzlgQO1op7rXZ0w4FEsMez/ppJOy9XXXXTfdcMMNaYUVVsi2O/5nypQp2a65c+d2PJTr9tZbb51NphRD8K+88soUgWcEoLFstdVWaa211sr1fgrrmwJ6gPbNdvfUBAgQIECAAAECBAgQIECAQBMLRDAZEwd1tsT7PcvLRz7ykWz11ltvTW1tbdn6l770pU7Dz0ceeSQbjh4ndjUJUlZQF//p169fdrR8z85OLffyvPHGG7OQ9rrrrstOLe/v7Dr7CXRXQADaXSnnESBAgAABAgQIECBAgAABAgQKJHDsscemWj00n3nmmXTOOedkNd12223TmDFjsvVZs2ZVav/UU09V1qtXYkKiz33uc5Vds2fPrqz3dGXw4MHZJXHf6dOnd3p53K9///7pnXfeSUcddVT2DtC4du+99+70GgcI9ERAANoTLecSIECAAAECBAgQIECAAAECBAoicPfdd6edd945xXD48nLLLbekbbbZJkXoGMPjTz311PKh9N73vjeVe2VGQBrnlgPUefPmpX/+85/pox/9aIpyy8vUqVPLqz3+HDlyZOWaCGvjPaMPP/xwZV95JWa033HHHbPNc889N/v85Cc/mYYPH14+xSeBdyUgAH1XfC4mQIAAAQIECBAgQIAAAQIECDRGYLPNNksxbDwCxJi9PWZl32677bJAdODAgenss89OG220UaVyq622WjryyCOz7ZhpPc6N2eR32GGHbEKkKG/ChAlZz8vy5EQvvPBCNjlSpZAerHzwgx+sBK4/+clPUvRGjUmYai3l+5WHyxv+XkvJvt4KCEB7K+c6AgQIECBAgAABAgQIECBAgEADBaIH5xe/+MUUw8WffPLJ7P2ZUZ3tt98+3Xzzze2GsperecIJJ6QII0eMGJHtiomRxo8fn73zc/3110/XXHNNuuSSS9K+++6bHY+epDE5UW+WCDxPP/30LKAtXx/vF6217LrrrlkIG8diYqYPfehDtU6zj0CvBGpPFdarolxEgAABAgQIECBAgAABAgQIECCwsASGDBmS9fI888wz0z333JO9Z3OdddapvPOzVj1iWPw3vvGNdMghh6R4D2i8L3SZZZZJ6623Xho2bFjlkrFjx1YmTKrsLK2MHj265v7qc6rXDz744BQ/MUz/7bffTiuuuGL14cp6vAO0PKN9+Z2glYNWCLxLAQHouwR0OQECBAgQIECAAAECBAgQIECgkQKDBg1KW221VY+qEL1GI/SMn4WxLLfccl3eJnqeTpo0KRsyH71aLQTyFDAEPk9NZREgQIAAAQIECBAgQIAAAQIECPRIIGaa/9GPfpRdEz1P11hjjR5d72QCCxLQA3RBQo4TIECAAAECBAgQIECAAAECBAjkKnD77benf/3rX9ls9VdccUX629/+lpXf2SRJud5cYX1OQADa55rcAxMgQIAAAQIECBAgQIAAAQIEGiswceLE7N2g1bX47ne/m81IX73POoE8BASgeSgqgwABAgQIECBAgAABAgQIECCwEARiEqKF9d7Oej5ODHNfdNFF08yZM7NJm/bZZ5907LHH1vOWyu7DAgLQPtz4Hp0AAQIECBAgQIAAAQIECBBoLoFvfetbKX6afdlss82yWetffvnlTmeGb/ZnVP/iCJgEqThtoSYECBAgQIAAAQIECBAgQIAAgT4jMGDAAOFnn2ntxj6oALSx/u5OgAABAgQIECBAgAABAgQIECBAgEAdBQSgdcRVNAECBAgQIECAAAECBAgQIECAAAECjRUQgDbW390JECBAgAABAgQIECBAgAABAgQIEKijgAC0jriKJkCAAAECBAgQIECAAAECBAgQIECgsQIC0Mb6uzsBAgQIECBAgAABAgQIECBAgAABAnUUEIDWEVfRBAgQIECAAAECBAgQIECAAAECBAg0VkAA2lh/dydAgAABAgQIECBAgAABAgQIECBAoI4CAtA64iqaAAECBAgQIECAAAECBAgQIECAAIHGCghAG+vv7gQIECBAgAABAgQIECBAgAABAgQI1FFAAFpHXEUTIECAAAECBAgQIECAAAECBAgQINBYAQFoY/3dnQABAgQIECBAgAABAgQIECBAgACBOgoIQOuIq2gCBAgQIECAAAECBAgQIECAAAECBBorIABtrL+7EyBAgAABAgQIECBAgAABAgQIECBQRwEBaB1xFU2AAAECBAgQIECAAAECBAgQIECAQGMFBKCN9Xd3AgQIECBAgAABAgQIECBAgAABAgTqKCAArSOuogkQIECAAAECBAgQIECAAAECBAgQaKyAALSx/u5OgAABAgQIECBAgAABAgQIECBAgEAdBQSgdcRVNAECBAgQIECAAAECBAgQIECAAAECjRUQgDbW390JECBAgAABAgQIECBAgAABAgQIEKijgAC0jriKJkCAAAECBAgQIECAAAECBAgQIECgsQIC0Mb6uzsBAgQIECBAgAABAgQIECBAgAABAnUUGFDHshVNgAABAgQIECBAgAABAgQIECBQUIEpU1J6+OGU4nPRRVNaZZWU1lorpX79Clph1SLQSwEBaC/hXEaAAAECBAgQIECAAAECBAgQaDaB2bNTOueclC64IKXbb09p3rz2T7Diiil98pMpHXFESquu2v6YLQLNKiAAbdaWU28CBAgQIECAAAECBAgQIECAQA8Ebr01pQMPTOmJJ/5z0QorpLTZZimNHp3Sm2+m9PTTKf3jHymdfnpKZ531nxD0Bz9IqX//HtzEqQQKKCAALWCjqBIBAgQIECBAgAABAgQIECBAIE+B6PX55S+nFD1Ad9wxpWOOSWnLLecf7v7qqymdeWZKP/5xSieemNLdd6f0u9+ltPjiedam+2U9/vjjaZ999pnvgn6lcfqLlsbtDx8+PK233nrpK1/5SqnHqi6r80HlsGPu3Lnp+eefb2pfkyDl8EVQBAECBAgQIECAAAECBAgQIECgqAJXXZXSQQf9J+z89a9Tuu66lLbaav7wM+q/9NIpHX10Sg88kNKGG6Z0440pfeYz8w+VX1jP+vbbb6d77rlnvp+7S8nshAkT0tVXX10Ka39cenfpWmn//fdP06ZNW1hV6xP3CedNN900XXLJJU39vC3XA3TmzJnpiiuuKP0Lxd1p6tSpac0110wbb7xx2nnnnUtdtnvWZ3v8+PHp/vvv77SBR40alfbdd9/5jj/66KPp8ssvT88991waNmxY2mCDDdIOO+yQVltttfnOtYMAAQIECBAgQIAAAQIECBAgUC+Bl15Kpewipba2lC66KKU99ujenWJCpBgyH71E//KX//QI/e//7t619TrrzjvvzHp9RvltpQd666230ssvv1wKdK8rvdf0nHTeeeeVerjOLr3f9IJ6VaHPlXv++edn2dinP/3ppn72lgpAX3/99XTwwQeniRMnZo2y5JJLpmuv/f/s3QecFOX9x/Hv3NE7ohSlFxXFgiigiKDSqxo1UWNJjCUx0ahYEo3Y81dji1FjSWKKmmCldykWsCGKoIiASlEQkKL0u/k/v1nm2NubvTvuDm537/O8XsvuzjzT3rNc+d5TJgSPt9zIvsOHD1eVKlWKfcNGjhwZ/IUh2QYWaCYGoBa+Pvzww8EmtWrV0vbt2zVnzhyNGDHCNR3/vyA1T7Y/liOAAAIIIIAAAggggAACCCCAAAJlKWBd3W18z6uvLn74GR7f9S7XK6/EWoLefbd08cWSawtWbuVI1yS1WrVqBY7/I5fqnuOaqVrjt2effVZDhw7VWWedVaAeCyquQEYFoHfccUcQfnbp0kV/+MMfgnEgVqxYoZtuusn91WKm/vznP2vYsGHFvtuLdo0KfOWVV+b9hSF+49oJA2DMc+3D7RgWslrY2r17d+3cuVOvvvpq3rGfc39uaWyjC1MQQAABBBBAAAEEEEAAAQQQQACBvShgvcH/8Q+pXj25nKRkBzr00Fj3+ccei+3ruutKtp+9vdXJJ5+s2267Tb/73e/cWKeXa+DAgapRo0aBw1rjublz57qJoBYFPXWt13CDBg0K1Fu7dm3QuvQgN1NUPQdoXetff/11bdmyRT169FDDhg3ztrHeyLNnzw72aT2RLQ/KyooeddJaqFpv4wULFqhSpUpumIEjdcghhwSv83YY8cLOZ/r06UGrV+vub9mXjYMaX8J92vovvvgiON9u3boV6JG8detW2diqCxcuDK6rTZs2OtTd6CZNmuTtzsb9tB7O69atC5atWrVK8+fPdwH4/mrUqFFevbR54ZoMZ0RxN8E/8cQT/d69e/vuw5jvmtyHxHcfTt91Q/c3btyYb12yN+7GBvtzfzVIVqXA8muvvTbYxjW7LrDuoYceCtY9/vjjBdYVd8GaNWt89yH23fgWxd2EegggUEEFXDcQf+XKlf7XX39dQQW4bAQQQACBiibgel4F3/vs+5/7Za2iXT7XiwACeyjgGi35/fv338Ot0q/6889bx3ffv+ii0p37e+/F9tOtW+n2U5KtP/jgA3cNCh6JeU/i/iw3cY3SgrpuaMR8q10DNf/222/3XeiYtz/brxsu0b/lllt8F0zmq/+nP/0pqPfUU08F661eeB72/Otf/zqoP378eN/1AM63rnPnzv7q1avz7c/eWJ5juU78fuy1Cx99172/QH1b4MJa/6ijjvJd2JlvOzcuZ7AufiO79ubNm/svv/xycF3hce6///68ak8//bRfp06dfPuyei6wDa7Jjbka1LVcLNw+/tmyr3QsGdMC1FJwK5bCJzaHtq7w7sOnWbNmyX0wdfbZZwd1C/snbP1pKXxxig3K+8477wRV+/btW2ATW2bd48eMGaNf/OIXRSb7BXbAAgQQQAABBBBAAAEEEEAAAQQQQCBOwI32J9cwL2n5179iq3r3TlqlWCs6dZLq15fLVeSyDbk5VpJvZjPMu+lQyqVYS06bsMdaY1pryE524ruKZUEuGAxabroQT+3btw9abFpPXheMBq0bLbdJLC4I1eeff+7GTv2RXMO7vKEW//KXvwTjjf7DNbEdMGCALPexmdIfc01lLR+ynsl//etf83ZnuZW1SnXhoX784x9r0KBBys3NDXoNv+LGGTjhhBOCfffq1StvG5tbxgX1cg1bgi79Q4YMccMZbNK/3I21a7Tj2gRR8T2Nbf0lbsYry8asdautt31Yseu0HsvWivOyyy4Lrufbb78NsirrOW3X1KJFi6D3tPV6do359NJLLwUtSe3cXaPDtB3aMWMCUGuGa8WaAEeVMAC1ZsYlCUCtK7trPSoLU6PKJ598EnyImzVrpgMPPLBAFWtKbB8eazJt/yGYEKkAEQsQQAABBBBAAAEEEEAAAQQQQGAPBFyeFczoXtQmzZsXVaPo9baPDz+UC+IKr2ujCbZtW3idvbk2DAMtAA2LzQ9j4aets+7v8V24L7zwQh177LFB0Ddp0iT1sQQ3rlg3cZtlPhxS8Te/+U3QsO1vf/ubnnjiCd3gZoayOV/CYgGsjT9qDfDCYqGkzVBv4adrjalrrrkmXKULLrgg6Lp/6623BvPa2PCKVatWDdZbwGnhp4WWtj4sl156aTDhtmVRdg62Piw2IXhLN4PV4sWLg6Ehrbu7haH2bAGnFQtp48dIvdoNEOtatOrRRx8NJpCya61evbquuuqqIPy1rv/Wld7ep2vJmADUxvq0YuMyRJVweThBUlSd+GVhC1ALPu2D4Jpcy8Y/sBDzuOOOk40LGj9GRFHHt33bOdiH3s4hWQBqIauNqxBVbFsrdh42ZgQFAQQQKErAvsHy9aIoJdYjgAACCGSCgP3cHha+/4USPCOAQDIBa3lXkUrlyqW/2rLYR+nPoug9hPnPkiVL8ir/8Y9/DF5biBgfftpCy3bOPfdc3e1meXrggQcKBKA2LmYYfoY7POmkk2QBqI3BafPOxJeuXbsGb5cvXx7MC2PjfFoAa2NyWuO4qBDx97//vWy2dcuiRo8erTPPPDNoPGchrp2vhazxxXXHDybg/t///ufC5rbxq4LXN954YxB+2puwl/QPP/wQBK02Bmp8+BlufPrppwcBaDjmZ7g8U54zJgC1G2kl/KAn3iA3vkGwKKyXuD7xvTVvtvLvf//bNe3ODj6k9kOV/Qd67bXX5MaSCD5s4Qct3G+y49u+inMO9p/CmklHlfrW3twVC0LduBZRVViGAAIIFBDg60UBEhYggAACCGS4gE1GYQ8KAgggkEzAWsNZ46J0L64ntTp2TH4V48bJTbgjrVyZvE5x14T7cL3HVVgYuiu6KO5uy7yeBY9Wwpag9jpsDWr5TjiEoi0PSzj5j036k1hsgqDEYhMdWbHtEifIDgNWC9mtMYoFoNZS04p1b7dzSCyVHahN4rR06dK8utZS1Yo1wrPWmInFuqPbI6pY0JpYLOj95S9/mW+xtRa1yZCs1akN2Wgl/g+K+Sqn+ZuMCEDtQ2VfvKwkfvDC++MGpA1eFucHIQsz3eDpQX0bw8HGhgg/bLbcZhWz/zz214Enn3wy+DDbGKBWkh3f1oXnEJ6rLaMggAACCCCAAAIIIIAAAggggAACJRFwvaoLLW44Rxd6yY3hKLkGfiUurjd1EKIefrjkhsRM6WJdv62EPW+txWPYKMTGxiysWI9dy43CLuhW17qTJ5Zw9vW6desmrso3+3tYLwxAo/YV7qBVq1bBy7BHchiAukmNwirFfg6vPXEDC/1tTE9rvWpDRLrJc/OqWFCbySUjrs7NVBUElG42sKR/6Q2DTzcjVpH308LO5557LvgPYgPGhh9Y29DG97RBY88777ygabINbGsD1dbcNcKvm30y6f7Dc4j/j5RYuWnTpkkTfDv3sWPHur+0VM5rwpy4Pe8RQAABE4j/Q0vY5QEZBBBAAAEEMlnAGkWEP4vb7wfF+bk/kz24NgQQKFzAWuHF/65feO30XTt4sNy4krGJi+65p/CWm4Vd5fPPx9YOHVpYrfJfZ7mQzbtiJQwBbViUsNhYnXv6/SHq96n4fYb7jnoO61mOY6Ww4cns3K2EdcMWyoVtE2wQ8U9U7mTnYuONWt5lxfIn6wrvZpgPxkC1XsuWb2VqyYgA1G6OzWBlSX04TmbiDQuXh0Fl4vr49/YDk01mZI+oYs2ZjzjiiKAbvHWJtw+IHd+KjeGZrBTnHGxfyT5wa9euDQJQu4awO3yyY7EcAQQqtoCNJWy/CNoPdXy9qNifBa4eAQQQqCgC9gti2MLHfrnl+19FufNcJwIlE7CAyH73z/Ry0EFyM4BL1hXeJiR38/fscVm3Tm7iHrner5KbLyili83YbsGhdfc+5ZRTgnO17weW2dj3CJvgqGNhYwbspasLu8zbOKDJis34bqVhw4bBczjkYtilP1gY9481evnPf/4j66Jv3eeLKjbTvIWf1mp11KhRsnFM44tNEmXFfo/MxJIx/9vDADIMGRNvVhhMltUPQuEH0kJJK0Ud3+qU9TnYPikIIIAAAggggAACCCCAAAIIIIBAMgGb/8ey3t/9Tm4G9GS1opdbFvbTn0quF7mb+Vw6+ODoeqmw1MLFcDZ2m9E8vgFchw4dglO07t9R5emnn1b79u1dwLt3Et7w+BY8hi09489jw4YNebPGh8HkYYcdFlR56623nL+7AQll5syZsi79119/fcKa6LczZswIVgwaNKhA+GkrbPJvK4ljgIZ/KAhbpAaV0vCfjAlAw0Ayfpav+PsRLrcPdFFl4cKFwexbL7zwQtKqq1evDtZZk2Er4fGtFWpU82T7MNtMWvbBCZP/YEP+QQABBBBAAAEEEEAAAQQQQAABBPaSwJFHys1hItnc0QMGSG4kv2IV65HtJkd3wZxkWZx1oU+1YtmMTVJ97733yoYwtPlZDnYp7ZVXXpnvVIcPHx68txaiFhzGF2t5ec0118gmQCpsjM74bfb09VA3doC1PP3666+DGeXjw0TLkH77298GmdHhbpBVmyjJSqdOnWTz0lieZLO6x7fMtLlrbr755qDej20mrGKUcGLu192AsIkh5zjXRPieXTc4fjg12204J07YQrUYh0rJKhnTBf7UU0/VxIkTNWXKFPcf1P0PjSv2IbGZ263Yf4iiis2CZem/dR3t3LmzWtiowXHFgsz58+cHS+zDacXGBrVZtuw/zNtvv60TTzwxWB7+M23atKAZtiX4NWrUCBfzjAACCCCAAAIIIIAAAggggAACCOxVgRtukKyH9eOPSz16SNddF3vUrh192AkTYus//lguE5FGj5bcEJHlXuInnrYxLeODRDs5y1wsF9pvv/3ynWvPnj11+eWXu2EA/hp0Fz/zzDN1pEuG58yZo0mTJun7779Xly5dit2aMt/Oi/HGGsM99thjQRd8e549e7b69esXhJoWPtqERJYpTZ06NW8MUNvtgw8+qG7duumJJ54IWmhaIGpBpHVXt3Pu3r17EJ4W4xRkQel9990XjJFq13raaacFkz1Zy1A7ruVldh4WrlroGk7wZF3srfz973/Xu+++qyFDhgRz4xTnmKlUJ2NagHbt2jVI6m22rPH254m48uyzz8q6qluQaTc5vrz55puaPHmyli5dmrfYbnq9evVk/5meeeaZfMm4JeGWiluTZfsQ2gc0LOecc07w8h//+Ee+sUjtLxLP7xox2AaYpSCAAAIIIIAAAggggAACCCCAAAL7UsDlbnroodgR77hDatw4NjO8tQ51k4IH66zhpOVdNm6ohZ82jKbLvNyEQvvyTJMfy1ouhg8LP22YQ+tebqGcdW+3AK9JkyaRO3jcpb+WD1kDthEjRgQtKC1ItP1cddVVQZYU320+cielWGi51bx584JWnXaedzt467JvjezOP//8oOGezTkTX6wX88fuRgwcODAIa+9wN+5f//pXMOmtnfNol0wXd/Z2czIjm23egt9bbrlFN7hk3FrQWjBqoWzYmM/GCw3LRRddFEyWZMf58MMP8xoYhuvT5dlzIZ+fLidb1HlaM2a7gfbhtQFgrau5fbhmzZoVJOgPuf/plvDHl7PPPjtogmzjJthsWGGxD4A1gTYe+w9lA+XaLHF2DBuA1j4w9gGJ/3DacX/5y1/qk08+Cf5D2TnYf0z764MFsBaY2gc8HD8hPFZxn20fNkGSXUd/+2pEQQABBJIIxE+C1Nh+sqEggAACCCCQ4QLxkyDZjL1lNfZ/hrNxeQhUWIFbb73VdQV/x00O5GYHqmDFJkm/7TbJRv3btCn64o87Tvr97+VaCUavT/ellq/Y8If2vcLynaiZ3vfmNW7bti3IjmrVqqVwsqOijmfbLFiwIMiUDjnkkBKfs2VX1op05cqVwQRKyQLjxPPZvn17kJ9ZfZtsMN1KRgWghm+DtlrI+M033+TdCxvD4eqrr9YxxxyTtyx8kSwAtfXWzf3hhx8OPpRhfRv7wAaktXA0qiu7fSCtibI1oQ7HArXg9PTTT9dll11W4g+oHZ8ANLwLPCOAQFECBKBFCbEeAQQQQCDTBAhAM+2Ocj0I7F2BihyAhrIuvpDNi+PajcmNBOi6Q8v1rJVrUCbtmu4krMozAmkvkHEBaHhHLCy0CYlsciJr/VTSVpe2Pxv7wPZlY000a9asWPuylp+LFy8OWpDaNmXRjJoANLy7PCOAQFECBKBFCbEeAQQQQCDTBAhAM+2Ocj0I7F0BAtC968veEUg1gYyZBCkRtkGDBrJHWRQb+DUc/LW4+7OxEaxJMgUBBBBAAAEEEEAAAQQQQAABBBBAAAEEyk8gYyZBKj9CjowAAggggAACCCCAAAIIIIAAAggggAACqSpAAJqqd4bzQgABBBBAAAEEEEAAAQQQQAABBBBAAIFSCxCAlpqQHSCAAAIIIIAAAggggAACCCCAAAIIIIBAqgoQgKbqneG8EEAAAQQQQAABBBBAAAEEEEAAAQQQQKDUAgSgpSZkBwgggAACCCCAAAIIIIAAAggggAACCCCQqgIEoKl6ZzgvBBBAAAEEEEAAAQQQQAABBBBAAAEEECi1AAFoqQnZAQIIIIAAAggggAACCCCAAAIIIIAAAgikqgABaKreGc4LAQQQQAABBBBAAAEEEEAAAQQQQAABBEotQABaakJ2gAACCCCAAAIIIIAAAggggAACCCCAAAKpKkAAmqp3hvNCAAEEEEAAAQQQQAABBBBAAAEEEEAAgVILEICWmpAdIIAAAggggAACCCCAAAIIIIAAAggggECqChCApuqd4bwQQAABBBBAAAEEEEAAAQQQQAABBBBAoNQCBKClJmQHCCCAAAIIIIAAAggggAACCCCAAAIIIJCqAgSgqXpnOC8EEEAAAQQQQAABBBBAAAEEEEAAAQQQKLUAAWipCdkBAggggAACCCCAAAIIIIAAAggggAACCKSqAAFoqt4ZzgsBBBBAAAEEEEAAAQQQQAABBBBAAAEESi1AAFpqQnaAAAIIIIAAAggggAACCCCAAAIIIIAAAqkqQACaqneG80IAAQQQQAABBBBAAAEEEEAAAQQQQACBUgsQgJaakB0ggAACCCCAAAIIIIAAAggggAACCCCAQKoKEICm6p3hvBBAAAEEEEAAAQQQQAABBBBAAAEEEECg1AIEoKUmZAcIIIAAAggggAACCCCAAAIIIIAAAgggkKoCBKCpemc4LwQQQAABBBBAAAEEEEAAAQQQQAABBBAotUClUu+BHSCAAAIIIIAAAggggAACCCCAAAIIpJ2Av327tGSp9N13UtWqUovm8ho0SLvr4IQRKEqAALQoIdYjgAACCCCAAAIIIIAAAggggAACGSLg+778V0bK/89z8idOljZvyX9lRx2hrNOHyrvicnn7759/He8QSFMBAtA0vXGcNgIIIIAAAggggAACCCCAAAIIILAnAv6HHynnkl9K774f26x2LemErvIaNZS+/0H+4iXSh/OU6x564GFl3XKTsq797Z4cgroIpKQAAWhK3hZOCgEEEEAAAQQQQAABBBBAAAEEECg7gVzX6jP3/J9JP2yWOnVU1vCb5fXpJc+6vscVf+Fnyv3L4/KfeEq5w26U/867yvrn3+RVqxZXa9+9/PLLL/WjH/0o6QErVaqkOnXq6KCDDtLJJ5+s888/X57nJa2faiv+9a9/6c9//rPOOOMM/f73vy/26R177LFB3enTp6tWLRdku9KzZ099//33GjNmjBo3bhws45+YAAEonwQEEEAAAQQQQAABBBBAAAEEEEAggwX8GTOV++PzpJwcZf3xDnk3DEsaEnqHHKzsRx6Uf+nFyhl6pvwRLynXhYzZz/6zXIS2bt2q99/f1WK1iDN45plnNHbsWP3zn/9UtXIKbIs4xQKrv/nmm+D6OnXqVGBdYQtCk507d+ZV++CDD7Rx40Ztt7FdKfkECEDzcfAGAQQQQAABBBBAAAEEEEAAAQQQyBwBf9065Zx5jrRjp7KeekxZv/h5sS7OO6KDsmfNUE6X7vKf+59yux2vrF9dXqxt91alL774QvXr18+3ewv7li9frldeeUX33XefRowYEbSI/Nvf/pavXkV4c9lll8kC49q1a1eEy92jayQA3SMuKiOAAAIIIIAAAggggAACCCCAAALpI5B75x+lNWvlXXxRscPP8Oq8Ro2U/fL/lHPcCcod7lqO/vRcea67eXkV6+puj8Syv5us6eijj1aTJk30y1/+MugCbpM9pVNX+MRrKsn7e++9tySbVYhtCEArxG3mIhFAAAEEEEAAAQQQQAABBBBAoKIJ+D+4iY0ef1KqWUNZd91Wosv3juko74Kfyn/m38HDu/KKEu1nX2xk42haALp69Wp99NFHOuqoowocdv369Zo7d64WLVqk1q1bB8FpgwYNCtRbu3atrHu6har77bdf0Mr0rbfeClpYduzYUUcccUSBbb799tvg2La/qDE4v/rqK23atElNmzZV3bp1C2xvC2zM01mzZqlKlSrq3LlzUDeyYsTCTz/91I1ykKODDz5YlStXLlDDrsnGDP3BfS6sTpcuXZKGxNaS9LPPPtPChQu1YcMGtWnTRoceemjgkbjjTz75RFlZWTrkkENkwbNt8/bbbwdu1rX/wAMPTNxkn7/P2udH5IAIIIAAAggggAACCCCAAAIIIIAAAntdwJ84Wdq6Td7QwW6m90YlPl6WGw/Uiv/qqBLvY19s+Pnnn+cdpkaNGnmv7YUFg3fccYcOOOCAYLKkSy+9VL169VIj5zJ8+HDFj6Vp9W080Q4dOug///mPfv3rX6tZs2b68Y9/rAsvvFBHHnmkBg0apG3btlnVvPLYY48F2/zf//1f3rL4F7Yf2+eoUQUdc3NzNWzYMLVs2VLnnHNOMPGTHfPcc88NAsv4/SR7bYGm7f/rr7/OV+XDDz8Mgl679jPPPDO4huOPP142kZKtSyw2fIC5WIB89tln65JLLtEpp5wShLG/+c1vtGXLlnybWFBrEzCtWLEisGnfvr0uuugiDRkyJNjmpptuCvzzbbSP39ACdB+DczgEEEAAAQQQQAABBBBAAAEEEECgLAT8+Quk775Luiv/+RHBOq9v76R1irWiS2e5vufyX39TuW5CJS87O/lmx3YqlxnjbfKf22+/PTgva2FpLRbjiwV5L7/8sho2bKhrr71WFtJZK1Cbgd22mz9/vl588cX4TYLXd999t1atWqWrrrpKp512WtAq9K677gomW7riiiv09NNPF9imJAts4qYdO3bo4osvVv/+/YMQ8/7779fzzz+vJUuWBK1CS9Kl31qUhvs766yzglDSWqHa7POzZ8/WgAEDgkmYwharZmGBsA0rYGOKnnjiibKWrTaz/MyZM/WXv/xFLVq0CMLa+Ou02ee7desWtAC1fVjY+tprr+mFF16QGbZq1Uq/+MUv4jfZp68JQPcpNwdDAAEEEEAAAQQQQAABBBBAAAEEykYg99obFLTyLGJ3XssWRdQofLXnujerRTNp3nzl9uxTaOXsRR9LbdsWWqekK601Zs2aNfNtbt3dLSCcMmWKli5dGnTFfvzxx4PnsOKECROC8NNCPuv+bq0bw2ItOq0l5EsvvaRJkyapT5/812fh50MPPRQEoOE2AwcO1DHHHCNrKdmjRw+df/754aoSP1v4+eCDD+q3v/1t3j5+8pOfBMex7uQWzlqAuafFAk5rEWqh5q233pq3ubWAtW781n39iSeeCNZbt3cLOK1Ya9b441199dVBS9hHH300aBVrrVXjiwWg1tXdZqIPW99efvnleduYVXkGoHSBj79bvEYAAQQQQAABBBBAAAEEEEAAAQQyTaB69dJfUVnso5RnceWVVwYtJK2VZPj43e9+p6eeeioIP63L9uuvvx50T48/1B//6CaCcsUCwPjw05bZeJ3WzdzKAw88EDzH/2MtSa2lZ3yxWdbtuFaiurPH1y3uaxuT07qXxxdrhXn99dcHiyzU3dNiY44uWLAguOYbbrgh3+bZrhXvww8/HDi23RVY29igt912W9BiMz78DDc8/fTTg5fr1q0LF+V7tq7uYfgZrrBWs1YsnC7PQgvQ8tTn2AgggAACCCCAAAIIIIAAAggggEAJBbz+faVmTZNu7c98Xfrsc/lffyMvaa1irli5a1zJC38qr3LBCXby9pJkcp+89aV4YQFhtWrVgm7W1uV94sSJwaRBFmqOHj1axx13XOTeLQS0YqGfTQKUWGyiIys2iVBisXFCK1UqGJ9Z13Ar1uKxLIqNsWnnl1hOOumkYJG11NzTYq1drZhL9YgAu3fv3rJHWCwMtkmk4st3bogFmwxp3rx5QTd4W5c4XmpY3yZJSizNmzcPFm3fvj1x1T59X/AO7tPDczAEEEAAAQQQQAABBBBAAAEEEEAAgZIIZF3160I3y/3zo8q96lr5b82ShgwqtG5hK/1ly6TlK6R2bVTpmbIZ87Kw4yVbZ60T69evn7fagjjrfv7f//43CPJsjEqboCi+2Kzva9asCRbZZD6FlWXuOm1io6pVq+ZVs/Euo4pNUGTFut9b9+9atWpFVSv2sqKOYzPSWwvNxCEACjtAGICGIWRhdcN1NlmUDQdgXdY/+uijYMzTcF1UEByus+cwSI5fZrPZW7HZ4cuzEICWpz7HRgABBBBAAAEEEEAAAQQQQAABBPaSgDdkoGQB6IuvyL/7DgVjeZbgWP6Il4KtvFKEqCU4bJGbWCBn44LaGJczZswIJvh59913gwl4wo3jgzebnT0M5ML1JX3evHlzsKm1SI1qXRm138RZ4+Pr2CzwUSVsOWmhbHGPE+7HwkwrNr5ocYpZXXDBBXruueeC6jaZlHWFt6EFbJzUOm4irBNOOCHprrJsrNgULQSgKXpjOC0EEEAAAQQQQAABBBBAAAEEEECgNAJey5ZSj+7SjNflP/MveT+/aI9357sZw3PvvV/K8pR1Yekn+9njEyhiA+s2bjOoW8tPm/HcZnu32cfDGdOtxaiNpWmtQG2Co44dOxaxx/yrbRzNqBIuP/roo/O6rodd2JMFjtZaNFmxyZaiil2TFZtFfU8DxnBsz+XLl0ftWjbpkQXINs7pySefrFdeeSUIP+u6YQxsbNOw+3248csvvxy8TBbWhvVS8Tl1o9lU1OKcEEAAAQQQQAABBBBAAAEEEEAAgTQSyL7nruBsc4fdKH/R53t85rmX/kpa/a28886Rd0SHPd5+X2xg3cdtpnYrNsbnX//613yH7dAhdt7WtTuqPP3002rfvr1sRvjEYuOMRo15abOyW+nUqVPeJvXq1QteR4WZ1n39iy++yKub+MJmqo8qI0aMCBbvaXBrGx122GHBtm+99ZZsKIDEYkMG2LAA4URL1orWyqBBgwqEn7Y8HO80ysPWp3IhAE3lu8O5IYAAAggggAACCCCAAAIIIIAAAqUQ8Lp0lnfDtdJ365XTb7D8TxcWa2++G18z54qr5P/3BallC2U9cG+xtiuvSj/72c906qmnBoe/8cYbtWKFG7N0Vxk+fHjw6k9/+pMs9Isv1sLymmuuCSZAamktZhOKzV5+112xEDlcNWfOHD3yyCNBl/SLLrooXKxDDjkkeD1y5EiF42/aAuuK/uMf/zgySA03tomG/vCHP4Rvg+fZs2fLZn+3rv633HJLvnXFeWPhbN++fbVhwwaZSXzLTQtkb7755mA3dm5WrIu7lddff73AuY4bN0733HNPsN5ajqZboQt8ut0xzhcBBBBAAAEEEEAAAQQQQAABBBDYA4EsN/5n7tIvZGN55nTtrqzbh8u7/BJ5uyaoSdyV/977yvntMOlNN3nSAfsre9RL8lw38lQvTzzxhI444gjZDPFXXHGFXn311eCUe/bsqcsvvzxoGWpdvc8888ygy7wFmZMmTQomMerSpUteS8j466xRo4ZuvfVWvfHGG7L9WCBqY2RaCGitM+NbgNqM8QcffHAwa7p1H7fwsXbt2po8eXLQArN///4aP358/O7zXtt2d955p6y1ZvxxbNxQC0ajZljP27iQFw8++KC6desms7EWnHZOFvpad3abvKl79+767W9/G+zBgtD77rtP1r3fPE477bRgQihrGTp16lRZd3+bGMnCUwtVrat8uhQC0HS5U5wnAggggAACCCCAAAIIIIAAAgggUAIBm/wo67//Ue7B7dxkSPcEM8PLPdukRt6xx0iNG0turE8tXqLcca4r9jvvumm73YE6HqXsV0bISzITeglOZa9uYmNZWmtPa+1orTCtm7qFnVasJaWFfTfccEMQXIZdy21ioauuuirYLmqG9XPOOUft2rULwskpU6YE43B27dpVl112W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" width="672" /></p>
<pre class="r"><code>## partisan diffs in local election opinion 
leoContrastP &lt;- cces2.2
leoContrastP &lt;- subset(leoContrastP, c(Pres_Vote == &quot;Solid Democrat&quot; | Pres_Vote == &quot;Solid Republican&quot;))

leoContrastP$pid &lt;- factor(leoContrastP$pid)
leoContrastPD &lt;- subset(leoContrastP, pid==&quot;Democrat&quot;)
leoContrastPR &lt;- subset(leoContrastP, pid==&quot;Republican&quot;)

leoContrastPD &lt;- summarySE(leoContrastPD, measurevar=&quot;RCO321_p&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
leoContrastPD$office &lt;- &quot;local election official&quot;
leoContrastPD$party &lt;- &quot;Democrat&quot;
leoContrastPD$proportion &lt;- leoContrastPD$RCO321_p
leoContrastPD$RCO321_p &lt;- NULL


leoContrastPR &lt;- summarySE(leoContrastPR, measurevar=&quot;RCO321_p&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
leoContrastPR$office &lt;- &quot;local election official&quot;
leoContrastPR$party &lt;- &quot;Republican&quot;
leoContrastPR$proportion &lt;- leoContrastPR$RCO321_p
leoContrastPR$RCO321_p &lt;- NULL

leoContrastPDR &lt;- rbind(leoContrastPD, leoContrastPR)
leoContrastPDR$Pres_Vote &lt;- ordered(leoContrastPDR$Pres_Vote, 
                                    levels = c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;))

pd &lt;- position_dodge(0.1)
leoCDP &lt;- ggplot(leoContrastPDR, aes(x=Pres_Vote, y=proportion, colour=party, group=party, legend=&quot;right&quot;)) + 
  geom_errorbar(aes(ymin=proportion-ci, ymax=proportion+ci), colour=&quot;black&quot;, width=.1, position=pd) +
  geom_line(position=pd) +
  geom_point(position=pd, size=3, shape=21, fill=&quot;white&quot;) + 
  xlab(&quot;&quot;) +
  ylab(&quot;&quot;) +
  scale_color_manual(values=c(&quot;Democrat&quot; = &quot;blue&quot;, 
                              &quot;Republican&quot; = &quot;red&quot;)) +
  scale_x_discrete(breaks=c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;),
                   labels=c(&quot;Solid\nDemocrat&quot;, &quot;Solid\nRepublican&quot;)) + 
  ggtitle(&quot;Local Election Official&quot;) +
  ylim(0,1) +                        
  theme_bw() 

leoCD</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>## partisan diffs in local election opinion 
sherContrastP &lt;- cces2.2
sherContrastP &lt;- subset(sherContrastP, c(Pres_Vote == &quot;Solid Democrat&quot; | Pres_Vote == &quot;Solid Republican&quot;))

sherContrastP$pid &lt;- factor(sherContrastP$pid)
sherContrastPD &lt;- subset(sherContrastP, pid==&quot;Democrat&quot;)
sherContrastPR &lt;- subset(sherContrastP, pid==&quot;Republican&quot;)

sherContrastPD &lt;- summarySE(sherContrastPD, measurevar=&quot;RCO323_p&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
sherContrastPD$office &lt;- &quot;sheriff&quot;
sherContrastPD$party &lt;- &quot;Democrat&quot;
sherContrastPD$proportion &lt;- sherContrastPD$RCO323_p
sherContrastPD$RCO323_p &lt;- NULL


sherContrastPR &lt;- summarySE(sherContrastPR, measurevar=&quot;RCO323_p&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
sherContrastPR$office &lt;- &quot;sheriff&quot;
sherContrastPR$party &lt;- &quot;Republican&quot;
sherContrastPR$proportion &lt;- sherContrastPR$RCO323_p
sherContrastPR$RCO323_p &lt;- NULL

sherContrastPDR &lt;- rbind(sherContrastPD, sherContrastPR)
sherContrastPDR$Pres_Vote &lt;- ordered(sherContrastPDR$Pres_Vote, 
                                     levels = c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;))

pd &lt;- position_dodge(0.1)
sherCDP &lt;- ggplot(sherContrastPDR, aes(x=Pres_Vote, y=proportion, colour=party, group=party, legend=&quot;right&quot;)) + 
  geom_errorbar(aes(ymin=proportion-ci, ymax=proportion+ci), colour=&quot;black&quot;, width=.1, position=pd) +
  geom_line(position=pd) +
  geom_point(position=pd, size=3, shape=21, fill=&quot;white&quot;) + 
  xlab(&quot;&quot;) +
  ylab(&quot;&quot;) +
  scale_color_manual(values=c(&quot;Democrat&quot; = &quot;blue&quot;, 
                              &quot;Republican&quot; = &quot;red&quot;)) +  
  scale_x_discrete(breaks=c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;),
                   labels=c(&quot;Solid\nDemocrat&quot;, &quot;Solid\nRepublican&quot;)) + 
  ggtitle(&quot;Sheriff&quot;) +
  ylim(0,1) +                        
  theme_bw() 

sherCD</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>## partisan diffs in local election opinion 
daContrastP &lt;- cces2.2
daContrastP &lt;- subset(daContrastP, c(Pres_Vote == &quot;Solid Democrat&quot; | Pres_Vote == &quot;Solid Republican&quot;))

daContrastP$pid &lt;- factor(daContrastP$pid)
daContrastPD &lt;- subset(daContrastP, pid==&quot;Democrat&quot;)
daContrastPR &lt;- subset(daContrastP, pid==&quot;Republican&quot;)

daContrastPD &lt;- summarySE(daContrastPD, measurevar=&quot;RCO324_p&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
daContrastPD$office &lt;- &quot;d.a.&quot;
daContrastPD$party &lt;- &quot;Democrat&quot;
daContrastPD$proportion &lt;- daContrastPD$RCO324_p
daContrastPD$RCO324_p &lt;- NULL


daContrastPR &lt;- summarySE(daContrastPR, measurevar=&quot;RCO324_p&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
daContrastPR$office &lt;- &quot;d.a.&quot;
daContrastPR$party &lt;- &quot;Republican&quot;
daContrastPR$proportion &lt;- daContrastPR$RCO324_p
daContrastPR$RCO324_p &lt;- NULL

daContrastPDR &lt;- rbind(daContrastPD, daContrastPR)
daContrastPDR$Pres_Vote &lt;- ordered(daContrastDR$Pres_Vote, 
                                   levels = c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;))

pd &lt;- position_dodge(0.1)
daCDP &lt;- ggplot(daContrastPDR, aes(x=Pres_Vote, y=proportion, colour=party, group=party, legend=&quot;right&quot;)) + 
  geom_errorbar(aes(ymin=proportion-ci, ymax=proportion+ci), colour=&quot;black&quot;, width=.1, position=pd) +
  geom_line(position=pd) +
  geom_point(position=pd, size=3, shape=21, fill=&quot;white&quot;) + 
  xlab(&quot;&quot;) +
  ylab(&quot;&quot;) +
  scale_color_manual(values=c(&quot;Democrat&quot; = &quot;blue&quot;, 
                              &quot;Republican&quot; = &quot;red&quot;)) + 
  scale_x_discrete(breaks=c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;),
                   labels=c(&quot;Solid\nDemocrat&quot;, &quot;Solid\nRepublican&quot;)) + 
  ggtitle(&quot;District Attorney&quot;) +
  ylim(0,1) +                       
  theme_bw() 

daCD</code></pre>
<p><img 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DWveU1k8DVTBr/ze/WmWnA1l0bIvs8gf1lfbdRrvPrVr47f/e53/1d69VueK1MGN2v/X1V+bPreGERt7M+mmZ0kQIAAAQIECBAgQIAAAQIE2hfo/qBSNY61QDemwOe0+dp3Tf2VU38Hp5Gmkd9www31srW1C/trAbHBxYvPv/zlL+v58n61oNGAfLX1NuvXa7vAD7jW+KFxmnTWUxu52V8LUtaz5FTs2s7o9c8jtT0znXTSSfX7zpo1q7+2FmO9bONBTlPefPPN63kbnVpte2N9rR7X1vWs3zOnXTdLtVGV9TxHHnlksyz1c622tdV8tWBx/d45HX9wv5Y3zj552cteVs+79dZb9z/wwAPl5eJ9tH3bOAU+vxcOOuig/lqAdUCd+aEWhO+vrRdbv/cOO+wwJE+eaPzzVAvk9tdGrDbNl9/jb3vb2+r1Zd2NKaewNy6lcNlllzVeHnCcSyqUf/7SpFzeYEAmHwgQIECAAAECBAgQIECAAIGuCBgBWotCrIspR0/mjuJlKjeVKT+v6T2nNZeptj5l041h8vq+++4btTU248ADDyzea8Gvsljb7znasLbuZdQCpvU6akHMYTcCqmdqOGjcPf3Nb35zMRW+4XL9MKfzv+c976l/zlGGY51qf7LjrLPOqt9muNGBjeezXTlScTzSHXfcEbU1WOu3OvbYY4sNfOonGg5yc6Za0Dg23HDD4mxuVJQjbYdLo+3bHK35yU9+csCSBWXdWVdu8FWmHN2bIzobU44ELked5vncUCo3TmqWciOnU045pRhtmtd//vOfR22d1nrWnMKeo4rLNNI0+K985StltnjFK14xYMmA+gUHBAgQIECAAAECBAgQIECAQFcEBEC7wtiblWQQtEy54/hoUq6DWKac6py7sQ+XMth4ySWXRL7nDu6dptqmSqPe2bvxnrkm6Y033licygDdO9/5zsbLQ45rIxiLKdE5rfrTn/70kOvdPvHTn/40ahvtFNVmkDeXF2iWctf7cq3SnJY9UsCtWfl2z/3+97+P8vulNnoxXv/6149YVe5s/u53v7ue58ILL6wfDz4Ybd/uv//+9YDk4Lryc+P3aX4evA5oLh2Qyy1kyrU+Mxg+Uso8OV2+TD/4wQ/Kw+K9cRr8ueeeW0z9H5Ch9iGXD2gMpA9e3mBwfp8JECBAgAABAgQIECBAgACBzgQEQDvz6+nSjaPhttlmm1E9S276UpbJ9R1f+MIXxtOf/vRiHcix3iyoNpV5VG0dnPnyyy+vn9p+++1j4403rn9udrDRRhtFjnLM9UZ32223Zlm6eq5xrcoXvOAFkQHEZinbdcghh9Qv5UjL8UjXXXdd/Ta58U8ZhK2fbHLwjGc8o362tmN6/XjwwWj7tvweHFxP+TnXdm0cKVwGbsvruTZomXK90sa85fnB740jRBvLZ778M7DtttsWRfLP1+AAaV7I/wzIdXIz5X8IjPaZi4K+ECBAgAABAgQIECBAgAABAi0LTGk5p4wTSqC2NuOA0WnbbbfdqJ/vzDPPjMaNkH72s59FvnJE5RZbbBG1tR3juc99bvGeIy27lXLDm05SbpJTpq222qo8rMR7Bs1y5GCZHvGIR8Q3v/nN8uOQ90033bR+LgPPGdxt3I28frGLB40B0DUFIMvblkHB/JybJGUA8JGPfGR5uf4+2r7NEahrShmgze/3TLW1NgdkbwxgXnvttdFKfStWrKjX0Vg+T/b19RWbQ33gAx8o8uSo3NxgqjHZ/KhRwzEBAgQIECBAgAABAgQIEBh7AQHQsTeu5B0y2NOY2gmA5qi+DLg17qZe1pm7g59xxhnFa/311y+mDZ988sktjRYs6xjuvZUg1XBl8/z8+fPrl7fccsv6cRUOMthZBuuyPaeffnrxarVtOQp0rAOg5ZTxbFOrAdBcAzSXXMip+plyin+zAOho+3bevHlFfe1+aQxg1jbViltuuWVUVd19993FtPq5c+fWy+WU9txFPtdyzZ3l77nnnvoaqLlOa7lcRK7Be/jhh9fLOSBAgAABAgQIECBAgAABAgTGRkAAdGxcK1/rFVdcUW9jThPOjYzaSY95zGMiRwSWG8Lk+o5//etfB1SVI/4+/vGPR97z29/+djE6dECGUX6YPHnyKEsMzN64+VNO369Sapz+3k67cvRoWq9pWn87dZdlcnOhMtV2Wi8PR3zPdS8b8zb2QWPBTvu2sa5WjhtHhO6yyy6Ra5CONmWgszFlUPiAAw6ISy+9tFgr9ZxzzokjjzyyyJLf/xlozZTLRuR/DkgECBAgQIAAAQIECBAgQIDA2AoIgI6tb2VrP++88+pt22uvvYbdxb2eaQ0HuZZhucFRbjCUgdALLrggLr744vqIxl/96ldxzDHHDNgAZg3VjsnlxjUXb7vttjG5RzuV5uZMv/71r+tFP/KRj0TjFPf6hUEHGWD+j//4j+JsBnRzB/k1bew0qIpRfcxp6hncy3TTTTcV72v6kiOCc8f1Mm2yySbl4Vp9z7Vsy5R/Dho3JyrPt/OemyGVRl//+tfrAdDGjape9apXtVO1MgQIECBAgAABAgQIECBAgMAoBQRARwk2EbLnLt6XXXZZ/VHe8Y531I+7cZDrPb7xjW8sXjn9N49zFFymDIjmiLlcK3FtpcYA6K233tpSM9Io1zHNZ8t1T3NKd7dTBi7LtNNOO40qiPmNb3wjfvGLXxTFP/e5z8XRRx89ZsaN63S2GgBtzDdp0qTItU2rkBoDoFdddVXXmpTrfuaO8hmczmUi/vnPf0aOtP7pT39a3COXXmjcGKprN1YRAQIECBAgQIAAAQIECBAgMETALvBDSCb2iRwh+LrXva7+kBkMHLxJS/3iCAcf+9jHivULc8pwOdKtWfYMFH7hC1+ojzDNQNA111zTLOu4nWsMgOZalI3rQDZrRG6adOqpp8aHPvSheMtb3lIEsprl6+Rcbqzzla98pV7FK17xivpxKwevec1r6tnymS666KL6524fNAZAM6C9YMGCNd6iceTjfvvtF43T6NdYeAwzNAZAc3f6tFtTymDzE57whOL7v9zsaHCZ3E3+pS99aXE6p/9///vfL0ZEl6Ngs38zECwRIECAAAECBAgQIECAAAECYy/gN/CxN67MHW644YZ4wQteEL/97W/rbcqp0u2su5gb4eR6kxnMPPvss+v1NTvIdRYb10ls3DAmA0VlGq/1OHOjnac+9anFbTM4lYHNkVJjYPKZz3xmzJkzp8jezbZngCyDw5lydOwRRxxRHLf6JYNtuclQmT772c+Wh8V7q21tJd+znvWs2GqrrYp6c1Ofk046acC9Bn/IoGLj6NYqbfyz5557Rk59z5TfpxngHillADODnldeeWXx/d+4odbgcjkNvky58dH3vve98mOxU3z9gwMCBAgQIECAAAECBAgQIEBgTAUEQMeUd+1WngHFu+66K3784x9HTuHedddd40c/+lG9US95yUuK3dnrJ0ZxkGXLlCM8c73P4dIJJ5xQD4DmRjNbbLFFPWvjZjgZgPrd735XvzaWB7lRUDkCL0cnDrf5UAaLjz322HpTcofvMnWz7Y33z7VUcyOd0aQMfjb2SQZUG9c3bbWtreTLIGmOiC3TJz7xichp981SBt0PO+yw+vqf2fejHd3arN5uncvvgU9+8pP15QLyz8d73vOeensb75NB/Fy3s9zkKwPVb33rWxuzDDh+0pOeFLmUQaYcKVv+GckRsI0jTwcU8oEAAQIECBAgQIAAAQIECBDouoAAaNdJx7/CjTbaqJhSnNOKy1euN5hrVm622WaRoxYzYNU4wjJHDObmLFOmtLcMbO5y/exnP7t42AxcPu95zytGteUotz/+8Y/FCLkcGZqjBTPYmCnvlaMFG9f/zGBatrFMBx54YFHXc57znLjjjjvK011/33vvvaMcoZej+l772tcWU5YzGPqHP/yhmEKeo2MziFXuXp55Gkcvdqvt+ZyNgel2A4SN0+BzZGsGpsvUaltbzZfLJmRfZXrwwQeLdV5zdHEGcnMtzR/+8Idx3HHHRTrnmrOZMtiYvvPmzSs+V+VLBiRf+cpX1puT36NPfOIT49Of/nSxrmoGwb/0pS8VI0UbN0k68cQTY+edd66Xa3ZQfo/l91CuB5rJ5kfNpJwjQIAAAQIECBAgQIAAAQJjKFAb1ST1mEBtJFl/7VuirVdtE5/+U045pb8W9FvjU9dGFNbvcdpppw3Jf9999/XXRnTW84zUpo033ri/tvHSkDryxHvf+96mdeRzlqkWEK3nqQXZytPDvq+p7VmwFpTqf/vb395fC8jW6x7uGWpB5CL/4Bu20vbBZQZ/rk3Br9+/FsDuv/feewdnaflzbcRhva7NN9+8v7a2aL1sq21tNV8toNdfW0+2fr/h7PJ8bdOj/gsvvLDelsaD0fZtbROq+j1rQcrGqpoeb7DBBvX8taUbmuap/edAf23kZ39tOYh63pGepxYcb1rP4JO33377gDprAeb+/HMjESBAgAABAgQIECBAgAABAuMnYARoLcoxUVNuQLTbbrtFjqZ8/etfX2zEcv311xc7hLez7udgp1pgqVhPNEd4Nk5rb8yXozsPPfTQ+M1vfhP7779/46X68Qc/+MGoBSKjFrCrn8uDXGd0LFOOlv3oRz9aLBGwxx57NF0LNXfrzk1vcgpz5h+cutH2xvUxDznkkGhcI3Xw/db0uXEUaI4sPf/88+tFWm1rq/lyLdQcZZqjPfP7rNn3VC3wXSy/kKNADzrooHpbqnYwderUYi3YK664ohj126yvs8056jnzfOQjH2npEfJ7+uCDD67nzVGy+edGIkCAAAECBAgQIECAAAECBMZPoC9jreN3O3eaqAI5jfzWW2+NW265pXhl8DWnPz/ykY8c1SPfeeedka8MPNZGDY6qbKeZc5rytddeW7zy3rleabajccr+SPdYm20fqV3NrrXa1lbz5T3S77rrris2xpo1a1bkZlOPfexjI4OLvZZyCYG//e1vcfXVVxfrgW6//fax3XbbRS43MdqUywWcd955RbGLLrqoWBZitHXIT4AAAQIECBAgQIAAAQIECLQvIADavp2SBAgQGFHg7rvvLkY25zqpubFVbgrVakB9xIpdJECAAAECBAgQIECAAAECBFoWMAW+ZSoZCRAgMDqB3Dwpg5+ZcnkCwc/R+clNgAABAgQIECBAgAABAgS6IWAEaDcU1UGAAIFBAr/85S+L9Xdrmx7F9OnT4+abb45NNtlkUC4fCRAgQIAAAQIECBAgQIAAgbEWmDLWN1A/AQIE1gWBu+66K2o71BdT3XOzsauuuipWrFhRPPqb3vQmwc914ZvAMxIgQIAAAQIECBAgQIBAJQWMAK1kt2gUAQK9JpAbJ+Xu8fnemHbcccciGJobQ0kECBAgQIAAAQIECBAgQIDA+AtYA3T8zd2RAIEJKDB58uTYYost6k82ZcqUeOYznxmXXXZZCH7WWRwQIECAAAECBAgQIECAAIFxFzACdNzJ3ZAAgYkqMH/+/GK057Jly+KAAw6I9ddff6I+quciQIAAAQIECBAgQIAAAQI9IyAA2jNdpaEECBAgQIAAAQIECBAgQIAAAQIECIxWwBT40YrJT4AAAQIECBAgQIAAAQIECBAgQIBAzwgIgPZMV2koAQIECBAgQIAAAQIECBAgQIAAAQKjFRAAHa2Y/AQIECBAgAABAgQIECBAgAABAgQI9IyAAGjPdJWGEiBAgAABAgQIECBAgAABAgQIECAwWgEB0NGKyU+AAAECBAgQIECAAAECBAgQIECAQM8ICID2TFdpKAECBAgQIECAAAECBAgQIECAAAECoxUQAB2tmPwECBAgQIAAAQIECBAgQIAAAQIECPSMgABoz3SVhhIgQIAAAQIECBAgQIAAAQIECBAgMFoBAdDRislPgAABAgQIECBAgAABAgQIECBAgEDPCAiA9kxXaSgBAgQIECBAgAABAgQIECBAgAABAqMVEAAdrZj8BAgQIECAAAECBAgQIECAAAECBAj0jIAAaM90lYYSIECAAAECBAgQIECAAAECBAgQIDBagSmjLSD/2hN46KGH4oYbblh7DXBnAgQI9IhAf3//gJb29fUN+OwDAQIECBAgQGBtCvi3ytrUd28CBHpVYNNNN43111+/reb31X7wDvwtsa1qFBoPgbvvvjv222+/8biVexAgQIAAAQIECBAgQIAAAQIECBCojMCJJ54Yhx12WFvtMQK0Lba1W+gtb3lL7LPPPmu3Ee5OgACBCgssX748Fi9eXLRw5syZMWPGjAq3VtMIECBAgACBdU1g5cqVsXDhwuKxp06dGnPmzFnXCDwvAQIEWhZYsWJFvPa1r205f7OMAqDNVCp+bocddoh999234q3UPAIECKw9gSVLltR/qchfKGbPnr32GuPOBAgQIECAAIFBAvnL/IIFC4qz06ZNiw033HBQDh8JECBAoBTIAS6dJpsgdSqoPAECBAgQIECAAAECBAgQIECAAAEClRUQAK1s12gYAQIECBAgQIAAAQIECBAgQIAAAQKdCgiAdiqoPAECBAgQIECAAAECBAgQIECAAAEClRUQAK1s12gYAQIECBAgQIAAAQIECBAgQIAAAQKdCgiAdiqoPAECBAgQIECAAAECBAgQIECAAAEClRUQAK1s12gYAQIECBAgQIAAAQIECBAgQIAAAQKdCgiAdiqoPAECBAgQIECAAAECBAgQIECAAAEClRUQAK1s12gYAQIECBAgQIAAAQIECBAgQIAAAQKdCgiAdiqoPAECBAgQIECAAAECBAgQIECAAAEClRUQAK1s12gYAQIECBAgQIAAAQIECBAgQIAAAQKdCgiAdiqoPAECBAgQIECAAAECBAgQIECAAAEClRUQAK1s12gYAQIECBAgQIAAAQIECBAgQIAAAQKdCgiAdiqoPAECBAgQIECAAAECBAgQIECAAAEClRUQAK1s12gYAQIECBAgQIAAAQIECBAgQIAAAQKdCgiAdiqoPAECBAgQIECAAAECBAgQIECAAAEClRUQAK1s12gYAQIECBAgQIAAAQIECBAgQIAAAQKdCgiAdiqoPAECBAgQIECAAAECBAgQIECAAAEClRUQAK1s12gYAQIECBAgQIAAAQIECBAgQIAAAQKdCgiAdiqoPAECBAgQIECAAAECBAgQIECAAAEClRUQAK1s12gYAQIECBAgQIAAAQIECBAgQIAAAQKdCgiAdiqoPAECBAgQIECAAAECBAgQIECAAAEClRUQAK1s12gYAQIECBAgQIAAAQIECBAgQIAAAQKdCgiAdiqoPAECBAgQIECAAAECBAgQIECAAAEClRUQAK1s12gYAQIECBAgQIAAAQIECBAgQIAAAQKdCgiAdiqoPAECBAgQIECAAAECBAgQIECAAAEClRWYUtmWdaFhS5YsiaOPPjo222yzOPbYY9uq8c9//nOcc845cfPNN8esWbNit912i2c84xmx3XbbDVtfO2WGrcwFAgQIECBAgAABAgQIECBAgAABAgTaFpiwAdD+/v444YQT4k9/+lNMmdLeY5577rlx2mmnFbizZ8+OBx98MH73u9/F2WefHSeddFLsvffeQ+DbKTOkEicIECBAgAABAgQIECBAgAABAgQIEOiKwIScAr906dI4+eST4/LLL28bKQOnp59+ekydOjVOPPHE+OEPfxg/+tGP4q1vfWtk/Tmy9B//+MeA+tspM6ACHwgQIECAAAECBAgQIECAAAECBAgQ6KrAhAuAXnnllfHKV74y/vd//zcmTWr/8b785S9HjiJ9+ctfHk972tOir68v1ltvvTj88MPjsMMOixUrVsR3v/vdAZ3RTpkBFfhAgAABAgQIECBAgAABAgQIECBAgEBXBdqPEHa1Gd2p7OKLL46jjjqqGJm57777xtvf/va2Ks61Q3/9618XZQ8++OAhdZTnfvCDH8TKlSuL6+2UGVKxEwQIECBAgAABAgQIECBAgAABAgQIdFVgQgVA77nnnth8883j3e9+d5xyyikxb968trCuu+66YvTnlltuWdQ3uJKdd9455syZEwsXLoxbbrmluNxOmcH1+kyAAAECBAgQIECAAAECBAgQIECAQHcF2tsdqLtt6FptBx54YLz4xS9ue9OjsiG33357cTh37tzy1JD3vHb//ffHrbfeWuwI306ZIZXWTvz0pz+Ns846q9mlmD59enF+8eLFcffddzfN4yQBAgQIRDz00EN1hhyhv3z58vpnBwQIECBAgACBtS2Qy62VKTfb9ftdqeGdAAECQwXK3+dWrVo19GKLZyZUAHTjjTdu8bFHzvbAAw8UGUYKgK6//vpFnjJv+T6aMs1acdddd8UVV1zR7FJ9RGtOu8+/JCUCBAgQWLNABkMbA6JrLiEHAQIECBAgQGD8BDIY6ve78fN2JwIEek+gGz8jJ9QU+G51YY4WypTT3IdLs2fPLi4tW7aseG+nzHB1O0+AAAECBAgQIECAAAECBAgQIECAQHcEJtQI0O6QRMyaNauoaqQIczn8dtq0aUXedso0a+8hhxwST3/605tdKtYczes5+nSTTTZpmsdJAgQIEIhYunRpsUxJWuTP5/JnNBsCBAgQIECAQBUEclZf7mGRaerUqTHSTMIqtFcbCBAgsDYFyhhcX19f280QAG1CV06lX7RoUZOrq0/l+p+Zyl+q2ymzuqaBX2fMmBH5apamTFndXZMmTYrJkyc3y+IcAQIECNQE8udkmfzMLCW8EyBAgAABAlURaFzHLn+h9/tdVXpGOwgQqKJA+TOykwDow78hVvEJ11KbymBmGeRs1owyOFruNN9OmWb1OkeAAAECBAgQIECAAAECBAgQIECAQPcEBECbWJbTy3OH9xUrVgzJsXDhwmK6Qo4q2nHHHYvr7ZQZUrETBAgQIECAAAECBAgQIECAAAECBAh0VUAAtAnn5ptvHjvvvHMsXrw4fvWrXw3JcdlllxU7CmeemTNnFtfbKTOkYicIECBAgAABAgQIECBAgAABAgQIEOiqwDofAL388svj4osvjhtvvHEA7Mte9rLi81lnnVXfSCNPzJ8/P77xjW8U1w4//PDivfzSTpmyrHcCBAgQIECAAAECBAgQIECAAAECBLovsM5vgnTaaafFnXfeGa9//etj2223rQvnTuy77LJLXHfddfG6170uDjjggMid+i655JK4++6748lPfnI84xnPqOfPg3bKDKjABwIECBAgQIAAAQIECBAgQIAAAQIEuiqwzgdAh9PMHaY+8YlPxMc+9rG46KKL4mtf+1qRNc8fdthhceSRRw7YZTgvtlNmuPs7T4AAAQIECBAgQIAAAQIECBAgQIBA5wJ9/bXUeTUTu4Yc+fn3v/89kmrLLbeMWbNmrfGB2ymzpkpz5Ol+++0XH//4x+M5z3nOmrK7ToAAgXVWYMmSJZEb1mWaM2dOzJ49e5218OAECBAgQIBA9QRys90FCxYUDZs2bVpsuOGG1WukFhEgQKAiAsuXL4/dd989TjzxxGJQYjvNMgK0BbUpU6bETjvt1ELOh7O0U+bh0o4IECBAgAABAgQIECBAgAABAgQIEOiGwDq/CVI3ENVBgAABAgQIECBAgAABAgQIECBAgEA1BQRAq9kvWkWAAAECBAgQIECAAAECBAgQIECAQBcEBEC7gKgKAgQIECBAgAABAgQIECBAgAABAgSqKSAAWs1+0SoCBAgQIECAAAECBAgQIECAAAECBLogIADaBURVECBAgAABAgQIECBAgAABAgQIECBQTQEB0Gr2i1YRIECAAAECBAgQIECAAAECBAgQINAFAQHQLiCqggABAgQIECBAgAABAgQIECBAgACBagoIgFazX7SKAAECBAgQIECAAAECBAgQIECAAIEuCAiAdgFRFQQIECBAgAABAgQIECBAgAABAgQIVFNAALSa/aJVBAgQIECAAAECBAgQIECAAAECBAh0QUAAtAuIqiBAgAABAgQIECBAgAABAgQIECBAoJoCAqDV7BetIkCAAAECBAgQIECAAAECBAgQIECgCwICoF1AVAUBAgQIECBAgAABAgQIECBAgAABAtUUEACtZr9oFQECBAgQIECAAAECBAgQIECAAAECXRAQAO0CoioIECBAgAABAgQIECBAgAABAgQIEKimgABoNftFqwgQIECAAAECBAgQIECAAAECBAgQ6IKAAGgXEFVBgAABAgQIECBAgAABAgQIECBAgEA1BQRAq9kvWkWAAAECBAgQIECAAAECBAgQIECAQBcEBEC7gKgKAgQIECBAgAABAgQIECBAgAABAgSqKSAAWs1+0SoCBAgQIECAAAECBAgQIECAAAECBLogIADaBURVECBAgAABAgQIECBAgAABAgQIECBQTQEB0Gr2i1YRIECAAAECBAgQIECAAAECBAgQINAFAQHQLiCqggABAgQIECBAgAABAgQIECBAgACBagoIgFazX7SKAAECBAgQIECAAAECBAgQIECAAIEuCAiAdgFRFQQIECBAgAABAgQIECBAgAABAgQIVFNAALSa/aJVBAgQIECAAAECBAgQIECAAAECBAh0QUAAtAuIqiBAgAABAgQIECBAgAABAgQIECBAoJoCAqDV7BetIkCAAAECBAgQIECAAAECBAgQIECgCwICoF1AVAUBAgQIECBAgAABAgQIECBAgAABAtUUEACtZr9oFQECBAgQIECAAAECBAgQIECAAAECXRAQAO0CoioIECBAgAABAgQIECBAgAABAgQIEKimgABoNftFqwgQIECAAAECBAgQIECAAAECBAgQ6IKAAGgXEFVBgAABAgQIECBAgAABAgQIECBAgEA1BQRAq9kvWkWAAAECBAgQIECAAAECBAgQIECAQBcEBEC7gKgKAgQIECBAgAABAgQIECBAgAABAgSqKSAAWs1+0SoCBAgQIECAAAECBAgQIECAAAECBLogIADaBURVECBAgAABAgQIECBAgAABAgQIECBQTQEB0Gr2i1YRIECAAAECBAgQIECAAAECBAgQINAFAQHQLiCqggABAgQIECBAgAABAgQIECBAgACBagoIgFazX7SKAAECBAgQIECAAAECBAgQIECAAIEuCAiAdgFRFQQIECBAgAABAgQIECBAgAABAgQIVFNAALSa/aJVBAgQIECAAAECBAgQIECAAAECBAh0QUAAtAuIqiBAgAABAgQIECBAgAABAgQIECBAoJoCAqDV7BetIkCAAAECBAgQIECAAAECBAgQIECgCwICoF1AVAUBAgQIECBAgAABAgQIECBAgAABAtUUEACtZr9oFQECBAgQIECAAAECBAgQIECAAAECXRAQAO0CoioIECBAgAABAgQIECBAgAABAgQIEKimgABoNftFqwgQIECAAAECBAgQIECAAAECBAgQ6IKAAGgXEFVBgAABAgQIECBAgAABAgQIECBAgEA1BQRAq9kvWkWAAAECBAgQIECAAAECBAgQIECAQBcEBEC7gKgKAgQIECBAgAABAgQIECBAgAABAgSqKSAAWs1+0SoCBAgQIECAAAECBAgQIECAAAECBLogIADaBURVECBAgAABAgQIECBAgAABAgQIECBQTQEB0Gr2i1YRIECAAAECBAgQIECAAAECBAgQINAFAQHQLiCqggABAgQIECBAgAABAgQIECBAgACBagoIgFazX7SKAAECBAgQIECAAAECBAgQIECAAIEuCAiAdgFRFQQIECBAgAABAgQIECBAgAABAgQIVFNAALSa/aJVBAgQIECAAAECBAgQIECAAAECBAh0QUAAtAuIqiBAgAABAgQIECBAgAABAgQIECBAoJoCAqDV7BetIkCAAAECBAgQIECAAAECBAgQIECgCwICoF1AVAUBAgQIECBAgAABAgQIECBAgAABAtUUEACtZr9oFQECBAgQIECAAAECBAgQIECAAAECXRAQAO0CoioIECBAgAABAgQIECBAgAABAgQIEKimgABoNftFqwgQIECAAAECBAgQIECAAAECBAgQ6IKAAGgXEFVBgAABAgQIECBAgAABAgQIECBAgEA1BQRAq9kvWkWAAAECBAgQIECAAAECBAgQIECAQBcEBEC7gKgKAgQIECBAgAABAgQIECBAgAABAgSqKSAAWs1+0SoCBAgQIECAAAECBAgQIECAAAECBLogIADaBURVECBAgAABAgQIECBAgAABAgQIECBQTQEB0Gr2i1YRIECAAAECBAgQIECAAAECBAgQINAFAQHQLiCqggABAgQIECBAgAABAgQIECBAgACBagoIgFazX7SKAAECBAgQIECAAAECBAgQIECAAIEuCAiAdgFRFQQIECBAgAABAgQIECBAgAABAgQIVFNAALSa/aJVBAgQIECAAAECBAgQIECAAAECBAh0QUAAtAuIqiBAgAABAgQIECBAgAABAgQIECBAoJoCAqDV7BetIkCAAAECBAgQIECAAAECBAgQIECgCwICoF1AVAUBAgQIECBAgAABAgQIECBAgAABAtUUmFLNZmnVSAIPPvhgLFmyZKQsrhEgQGCdFsifk2VasWKFn5klhncCBAgQIECgEgIPPfRQvR157Pe7OocDAgQIDBFYvnx5ca6/v3/ItVZPCIC2KlWBfGVH51+OCxcurECLNIEAAQLVF1i2bFnkSyJAgAABAgQIVFFg5cqVfr+rYsdoEwEClREQAK1MV4xPQ/r6+oobzZ07NzbbbLPxuam7ECBAoAcF8j+KFi1aVLR89uzZkS+JAAECBAgQIFAVgQx6LliwoGjOtGnTYt68eVVpmnYQIECgcgJlALSMi7XTQCNA21GrQJlOOr0CzdcEAgQIjKlA48/IPG78PKY3VjkBAgQIECBAoA0B/1ZpA00RAgTWGYHyZ2T53s6D2wSpHTVlCBAgQIAAAQIECBAgQIAAAQIECBDoCQEB0J7oJo0kQIAAAQIECBAgQIAAAQIECBAgQKAdAQHQdtSUIUCAAAECBAgQIECAAAECBAgQIECgJwQEQHuimzSSAAECBAgQIECAAAECBAgQIECAAIF2BARA21FThgABAgQIECBAgAABAgQIECBAgACBnhAQAO2JbtJIAgQIECBAgAABAgQIECBAgAABAgTaERAAbUdNGQIECBAgQIAAAQIECBAgQIAAAQIEekJAALQnukkjCRAgQIAAAQIECBAgQIAAAQIECBBoR0AAtB01ZQgQIECAAAECBAgQIECAAAECBAgQ6AkBAdCe6CaNJECAAAECBAgQIECAAAECBAgQIECgHQEB0HbUlCFAgAABAgQIECBAgAABAgQIECBAoCcEBEB7ops0kgABAgQIECBAgAABAgQIECBAgACBdgQEQNtRU4YAAQIECBAgQIAAAQIECBAgQIAAgZ4QEADtiW7SSAIECBAgQIAAAQIECBAgQIAAAQIE2hEQAG1HTRkCBAgQIECAAAECBAgQIECAAAECBHpCQAC0J7pJIwkQIECAAAECBAgQIECAAAECBAgQaEdAALQdNWUIECBAgAABAgQIECBAgAABAgQIEOgJAQHQnugmjSRAgAABAgQIECBAgAABAgQIECBAoB0BAdB21JQhQIAAAQIECBAgQIAAAQIECBAgQKAnBARAe6KbNJIAAQIECBAgQIAAAQIECBAgQIAAgXYEBEDbUVOGAAECBAgQIECAAAECBAgQIECAAIGeEBAA7Ylu0kgCBAgQIECAAAECBAgQIECAAAECBNoREABtR00ZAgQIECBAgAABAgQIECBAgAABAgR6QkAAtCe6SSMJECBAgAABAgQIECBAgAABAgQIEGhHQAC0HTVlCBAgQIAAAQIECBAgQIAAAQIECBDoCQEB0J7oJo0kQIAAAQIECBAgQIAAAQIECBAgQKAdAQHQdtSUIUCAAAECBAgQIECAAAECBAgQIECgJwQEQHuimzSSAAECBAgQIECAAAECBAgQIECAAIF2BARA21FThgABAgQIECBAgAABAgQIECBAgACBnhAQAO2JbtJIAgQIECBAgAABAgQIECBAgAABAgTaERAAbUdNGQIECBAgQIAAAQIECBAgQIAAAQIEekJAALQnukkjCRAgQIAAAQIECBAgQIAAAQIECBBoR0AAtB01ZQgQIECAAAECBAgQIECAAAECBAgQ6AkBAdCe6CaNJECAAAECBAgQIECAAAECBAgQIECgHQEB0HbUlCFAgAABAgQIECBAgAABAgQIECBAoCcEBEB7ops0kgABAgQIECBAgAABAgQIECBAgACBdgQEQNtRU4YAAQIECBAgQIAAAQIECBAgQIAAgZ4QEADtiW7SSAIECBAgQIAAAQIECBAgQIAAAQIE2hEQAG1HTRkCBAgQIECAAAECBAgQIECAAAECBHpCQAC0J7pJIwkQIECAAAECBAgQIECAAAECBAgQaEdAALQdNWUIECBAgAABAgQIECBAgAABAgQIEOgJAQHQnugmjSRAgAABAgQIECBAgAABAgQIECBAoB0BAdB21JQhQIAAAQIECBAgQIAAAQIECBAgQKAnBARAe6KbNJIAAQIECBAgQIAAAQIECBAgQIAAgXYEBEDbUVOGAAECBAgQIECAAAECBAgQIECAAIGeEBAA7Ylu0kgCBAgQIECAAAECBAgQIECAAAECBNoREABtR00ZAgQIECBAgAABAgQIECBAgAABAgR6QkAAtCe6SSMJECBAgAABAgQIECBAgAABAgQIEGhHQAC0HTVlCBAgQIAAAQIECBAgQIAAAQIECBDoCQEB0J7oJo0kQIAAAQIECBAgQIAAAQIECBAgQKAdAQHQdtSUIUCAAAECBAgQIECAAAECBAgQIECgJwQEQHuimzSSAAECBAgQIECAAAECBAgQIECAAIF2BARA21FThgABAgQIECBAgAABAgQIECBAgACBnhAQAO2JbtJIAgQIECBAgAABAgQIECBAgAABAgTaERAAbUdNGQIECBAgQIAAAQIECBAgQIAAAQIEekJAALQnukkjCRAgQIAAAQIECBAgQIAAAQIECBBoR0AAtB01ZQgQIECAAAECBAgQIECAAAECBAgQ6AkBAdCe6CaNJECAAAECBAgQIECAAAECBAgQIECgHQEB0HbUlCFAgAABAgQIECBAgAABAgQIECBAoCcEBEB7ops0kgABAgQIECBAgAABAgQIECBAgACBdgQEQNtRU4YAAQIECBAgQIAAAQIECBAgQIAAgZ4QEADtiW7SSAIECBAgQIAAAQIECBAgQIAAAQIE2hEQAG1HTRkCBAgQIECAAAECBAgQIECAAAECBHpCQAC0J7pJIwkQIECAAAECBAgQIECAAAECBAgQaEdAALQdNWUIECBAgAABAgQIECBAgAABAgQIEOgJAQHQnugmjSRAgAABAgQIECBAgAABAgQIECBAoB0BAdB21JQhQIAAAQIECBAgQIAAAQIECBAgQKAnBARAe6KbNJIAAQIECBAgQIAAAQIECBAgQIAAgXYEBEDbUVOGAAECBAgQIECAAAECBAgQIECAAIGeEBAA7Ylu0kgCBAgQIECAAAECBAgQIECAAAECBNoREABtR00ZAgQIECBAgAABAgQIECBAgAABAgR6QkAAtCe6SSMJECBAgAABAgQIECBAgAABAgQIEGhHQAC0HTVlCBAgQIAAAQIECBAgQIAAAQIECBDoCQEB0J7oJo0kQIAAAQIECBAgQIAAAQIECBAgQKAdAQHQdtSUIUCAAAECBAgQIECAAAECBAgQIECgJwQEQHuimzSSAAECBAgQIECAAAECBAgQIECAAIF2BARA21FThgABAgQIECBAgAABAgQIECBAgACBnhAQAO2JbtJIAgQIECBAgAABAgQIECBAgAABAgTaERAAbUdNGQIECBAgQIAAAQIECBAgQIAAAQIEekJgSk+0chSNXL58eZx77rlx5ZVXxr333hs77rhj7LnnnvHsZz87Jk+e3FJNV199dVx00UUt5d1vv/3iiU98Yj3vZZddFn/84x/rnwcfbLzxxnHEEUcMPu0zAQIECBAgQIAAAQIECBAgQIAAAQJjIDChAqD33Xdf/Pu//3vceuutBdWGG24YP/rRj4rXL37xizjuuONi6tSpa2S86aab4jvf+c4a82WGjTbaaEAA9Hvf+1789re/HbbsdtttJwA6rI4LBAgQIECAAAECBAgQIECAAAECBLorMKECoP/1X/9VBD/33XffeP/73x8bbLBB3H777fG+970vfvazn8Xpp58eRx999BoFd99993jnO985bL4c4XnhhRfGzJkzY//99x+Q729/+1vx+a1vfWtMmzZtwLX8MGfOnCHnnCBAgAABAgQIECBAgAABAgQIECBAYGwEJkwA9Nprr41f//rXMWPGjPjgBz8Y06dPL8S22GKLOPXUU+NFL3pRXHDBBXHkkUeuMQi51VZbRb6apbvvvjvOPPPM4lIGWbfeeut6tvnz58eiRYuKUaGHH354/bwDAgQIECBAgAABAgQIECBAgAABAgTWjsCE2QTpJz/5SSH49Kc/vR78LElzKvw+++wTDz74YBEELc+3856jTO+555544QtfGE95ylMGVFGO/txpp50GnPeBAAECBAgQIECAAAECBAgQIECAAIG1IzBhAqDXXHNNIZjT35ulDIBmGmmDomblGs9dfPHFxfqeGVB9wxve0HipOB4cAF25cmURLB2S0QkCBAgQIECAAAECBAgQIECAAAECBMZFYMJMgc+1PjPNnTu3KVx5vtwgqWmmEU4uXbo0PvWpTxU5cqOl2bNnD8ldBkAz8HnUUUfFVVddFQ899FAx5f4JT3hC5LqguWnSSOkvf/lLXH755U2zZF2Zli1bFosXL26ax0kCBAgQiFixYkWdYfny5fVjBwQIECBAgACBKgiUv9tlW/LY73dV6BVtIECgqgLl73T9/f1tN3HCBEAfeOCBAqEMdA4WWX/99YtTZb7B19f0OUd/5vqfGcA88MADm2a//vrri/Nf+cpXYvLkybHzzjtHBkNvuOGGuPTSS+PKK6+M0047LXbYYYem5fPkH/7whzj55JObXp83b15xPgOg999/f9M8ThIgQIDAQIFc/iRfEgECBAgQIECgigL5O6Pf76rYM9pEgEBVBARA/68nVq1aVYyKzI/D7bJejtgs0Ubbieeff35R5NBDD40pU4bGjTOwescddxR5Dj744HjHO95RbMiUJ/L88ccfH7lR04c+9KH4/Oc/37SOorAvBAgQIECAAAECBAgQIECAAAECBAh0TWBoJK9rVY9fRZMmTSqCjTlNfbgAZ3l+6tSpo25YTm3Pqek5qvP5z39+0/K5+/zXv/71WLBgQey5557R19dXz7f55pvHCSecEEcccURkXblb/X777Ve/3njw+Mc/PnKjpWYp/2cwA6kzZ86MDTbYoFkW5wgQIECgJpAjPvPvhEzTp0+PadOmFce+ECBAgAABAgSqINA47T0H2MyaNasKzdIGAgQIVFKgjOk1xtpG29AJEQDNh954440j1/ccbupAeb6dv1i+//3vF665w/xwa3hmEHbLLbcsXs06YdNNN43ddtutmAafU+KHC4But912ka9mKafgZwA0g7gZBJUIECBAYHiBMgC63nrr+Zk5PJMrBAgQIECAwFoQyPXKy3U/c6CN3+/WQie4JQECPSOQPyczdRIAnTC7wGcANFMZ6Cw+NHxZtGhR8alcR7Ph0oiHud7mRRddVOR50YteNGLeNV3cZJNNiiwZyJQIECBAgAABAgQIECBAgAABAgQIEBh7gQkTAC2Dizm6slkqz++yyy7NLg97Lqer5/qej3zkI2OPPfYYNl9Okf/yl78c55xzzrB55s+fX1x71KMeNWweFwgQIECAAAECBAgQIECAAAECBAgQ6J7AhAmAljuzX3LJJUN0cpOk3IU9U67POZp09dVXF9l33HHHEYvde++9ccYZZ8QnPvGJuPnmm4fkveeee+Kaa64pzj/mMY8Zct0JAgQIECBAgAABAgQIECBAgAABAgS6LzBhAqBPfOITY5tttik2GbrgggsGSH3ta1+LnHa+9dZbx7777jvg2uWXXx4XX3xx3HjjjQPOlx9y5/ZM2267bXmq6XsGVufOnRv9/f3xpS99KXLDojLlNPqTTz652JDjyU9+cuy8887lJe8ECBAgQIAAAQIECBAgQIAAAQIECIyhwITZBCkXQn39618fxx57bHzoQx+KK664InLU5p/+9KfiODfBOOaYY4YsmHraaafFnXfeWZRtFuS8/fbbC/7hNiYq+yZ3GT7uuOPi7W9/e+Qo1N/+9rdx0EEHFTvH/+xnP4vbbrutCKIeddRRZRHvBAgQIECAAAECBAgQIECAAAECBAiMscCECYCm09Oe9rT42Mc+VgRAL7vssshXphwZmgdrXlkAAEAASURBVIHH3Xffvfjc6pecOp9T2zM1C44Orufxj398fOYzn4kMql533XXxrW99q8gyY8aMOPjgg4vgqN39Bqv5TIAAAQIECBAgQIAAAQIECBAgQGDsBCZUADSZ9tprr2Ijopzyfuutt0ZujrTZZpvFpEnNZ/ufffbZw+pmmZ/85CfDXm92Idf3/PznPx8LFy4s7j9nzpzYcssth71/szqcI0CAAAECBAgQIECAAAECBAgQIECgOwITLgBasmy00UaRr7WVNthgg8iXRIAAAQIECBAgQIAAAQIECBAgQIDA2hNoPixy7bXHnQkQIECAAAECBAgQIECAAAECBAgQINA1AQHQrlGqiAABAgQIECBAgAABAgQIECBAgACBqgkIgFatR7SHAAECBAgQIECAAAECBAgQIECAAIGuCQiAdo1SRQQIECBAgAABAgQIECBAgAABAgQIVE1AALRqPaI9BAgQIECAAAECBAgQIECAAAECBAh0TUAAtGuUKiJAgAABAgQIECBAgAABAgQIECBAoGoCAqBV6xHtIUCAAAECBAgQIECAAAECBAgQIECgawICoF2jVBEBAgQIECBAgAABAgQIECBAgAABAlUTEACtWo9oDwECBAgQIECAAAECBAgQIECAAAECXRMQAO0apYoIECBAgAABAgQIECBAgAABAgQIEKiagABo1XpEewgQIECAAAECBAgQIECAAAECBAgQ6JqAAGjXKFVEgAABAgQIECBAgAABAgQIECBAgEDVBARAq9Yj2kOAAAECBAgQIECAAAECBAgQIECAQNcEBEC7RqkiAgQIECBAgAABAgQIECBAgAABAgSqJiAAWrUe0R4CBAgQIECAAAECBAgQIECAAAECBLomIADaNUoVESBAgAABAgQIECBAgAABAgQIECBQNQEB0Kr1iPYQIECAAAECBAgQIECAAAECBAgQINA1AQHQrlGqiAABAgQIECBAgAABAgQIECBAgACBqgkIgFatR7SHAAECBAgQIECAAAECBAgQIECAAIGuCQiAdo1SRQQIECBAgAABAgQIECBAgAABAgQIVE1AALRqPaI9BAgQIECAAAECBAgQIECAAAECBAh0TUAAtGuUKiJAgAABAgQIECBAgAABAgQIECBAoGoCAqBV6xHtIUCAAAECBAgQIECAAAECBAgQIECgawICoF2jVBEBAgQIECBAgAABAgQIECBAgAABAlUTEACtWo9oDwECBAgQIECAAAECBAgQIECAAAECXRMQAO0apYoIECBAgAABAgQIECBAgAABAgQIEKiagABo1XpEewgQIECAAAECBAgQIECAAAECBAgQ6JqAAGjXKFVEgAABAgQIECBAgAABAgQIECBAgEDVBARAq9Yj2kOAAAECBAgQIECAAAECBAgQIECAQNcEBEC7RqkiAgQIECBAgAABAgQIECBAgAABAgSqJiAAWrUe0R4CBAgQIECAAAECBAgQIECAAAECBLomIADaNUoVESBAgAABAgQIECBAgAABAgQIECBQNQEB0Kr1iPYQIECAAAECBAgQIECAAAECBAgQINA1AQHQrlGqiAABAgQIECBAgAABAgQIECBAgACBqgkIgFatR7SHAAECBAgQIECAAAECBAgQIECAAIGuCQiAdo1SRQQIECBAgAABAgQIECBAgAABAgQIVE1AALRqPaI9BAgQIECAAAECBAgQIECAAAECBAh0TUAAtGuUKiJAgAABAgQIECBAgAABAgQIECBAoGoCAqBV6xHtIUCAAAECBAgQIECAAAECBAgQIECgawICoF2jVBEBAgQIECBAgAABAgQIECBAgAABAlUTmFK1BmkPAQIECBAgQIAAAQIECBBYGwK33XZb3HrrrWN+65UrV8Z9990XO+20U0ybNm3M7+cGBAgQWNcFBEDX9e8Az0+AAAECBAgQIECAAAEChcAZZ5wRxx9//LhpnH322XHggQeO2/3ciAABAuuqgCnw62rPe24CBAgQIECAAAECBAgQIECAAAEC64CAEaDrQCd7RAIECBAgQIAAAQIECBBYs8Bee+0Vr371q9ec8f9yrFixIr761a8WnzbaaKN4/vOf31LZVatWxbJly2LTTTdtKb9MBAgQINCZgABoZ35KEyBAgAABAgQIECBAgMAEETj00EMjX62mBx54oB4A3WabbeKLX/xiS0UzcLpgwYKW8spEgAABAp0LmALfuaEaCBAgQIAAAQIECBAgQIAAAQIECBCoqIAAaEU7RrMIECBAgAABAgQIECBAgAABAgQIEOhcQAC0c0M1ECBAgAABAgQIECBAgAABAgQIECBQUQEB0Ip2jGYRIECAAAECBAgQIECAAAECBAgQINC5gABo54ZqIECAAAECBAgQIECAAAECBAgQIECgogICoBXtGM0iQIAAAQIECBAgQIAAAQIECBAgQKBzAQHQzg3VQIAAAQIECBAgQIAAAQIECBAgQIBARQUEQCvaMZpFgAABAgQIECBAgAABAgQIECBAgEDnAgKgnRuqgQABAgQIECBAgAABAgQIECBAgACBigoIgFa0YzSLAAECBAgQIECAAAECBAgQIECAAIHOBQRAOzdUAwECBAgQIECAAAECBAgQIECAAAECFRUQAK1ox2gWAQIECBAgQIAAAQIECBAgQIAAAQKdCwiAdm6oBgIECBAgQIAAAQIECBAgQIAAAQIEKiogAFrRjtEsAgQIECBAgAABAgQIECBAgAABAgQ6FxAA7dxQDQQIECBAgAABAgQIECBAgAABAgQIVFRAALSiHaNZBAgQIECAAAECBAgQIECAAAECBAh0LiAA2rmhGggQIECAAAECBAgQIECAAAECBAgQqKiAAGhFO0azCBAgQIAAAQIECBAgQIAAAQIECBDoXEAAtHNDNRAgQIAAAQIECBAgQIAAAQIECBAgUFGBKRVtl2YRIECAAIGOBd71rndFf39/rLfeeh3XtaYKtt566zjuuOPWlM11AgQIECBAgAABAgQIEBhnAQHQcQZ3OwIECBAYP4Fzzz03li5dOi433HvvvQVAx0XaTQgQIECAAAECBAgQIDA6AVPgR+clNwECBAgQIECAAAECBAgQIECAAAECPSRgBGgPdZamEiBAgMDoBM4555yYNm1azJw5s6WCZ555Znzxi18s8h5//PHxzGc+s6VymWn27Nkt55WRAAECBAgQIECAAAECBMZPQAB0/KzdiQABAgTGWSCnpc+ZM6fl4ORFF11Ub+GjH/3o2G+//eqfHRAgQIAAAQIECBAgQIBAbwqYAt+b/abVBAgQIECAAAECBAgQIECAAAECBAi0ICAA2gKSLAQIECBAgAABAgQIECBAgAABAgQI9KaAAGhv9ptWEyBAgAABAgQIECBAgAABAgQIECDQgoAAaAtIshAgQIAAAQIECBAgQIAAAQIECBAg0JsCAqC92W9aTYAAAQIECBAgQIAAAQIECBAgQIBACwICoC0gyUKAAAECBAgQIECAAAECBAgQIECAQG8KCID2Zr9pNQECBAgQIECAAAECBAgQIECAAAECLQgIgLaAJAsBAgQIECBAgAABAgQIECBAgAABAr0pMKU3m71ut3rVqlXx0EMPrdsInp4AAQIjCOTPyTKN5mdmu+XKe3knQIAAAQIE1i2Bxt/L+vv7W/49rfHfHKMpt27peloCBAisFih/1ubPy3aTAGi7cmuhXNnRixYtivnz56+FFrglAQIEek/ggQceiHy1khrzLVy40M/aVtDkIUCAAAEC67DAkiVL6k+/cuXKtv7t8OCDD7ZVrn5jBwQIEJjgAsuXLy+esIyLtfO4AqDtqK2lMn19fcWdZ86cGXPnzl1LrXBbAgQIVF8g/4JcunRp0dDp06dHvlpJjflmzZrlZ20raPIQIECAAIF1WGC99darP/3kyZNb/rdDjma6//77i7JZR/67QyJAgACB5gJlALSMizXPNfJZAdCRfSp5derUqTFjxoxKtk2jCBAgUAWB/J/BMgCav1S0+jOz8ZcYP2ur0JPaQIAAAQIEqi3QOJV90qRJLf+bY8WKFfUA6GjKVVtD6wgQIDA2AvlzMlMnAVCbII1N36iVAAECBAgQIECAAAECBAgQIECAAIEKCAiAVqATNIEAAQIECBAgQIAAAQIECBAgQIAAgbEREAAdG1e1EiBAgAABAgQIECBAgAABAgQIECBQAQEB0Ap0giYQIECAAAECBAgQIECAAAECBAgQIDA2AgKgY+OqVgIECBAgQIAAAQIECBAgQIAAAQIEKiAgAFqBTtAEAgQIECBAgAABAgQIECBAgAABAgTGRkAAdGxc1UqAAAECBAgQIECAAAECBAgQIECAQAUEBEAr0AmaQIAAAQIECBAgQIAAAQIECBAgQIDA2AgIgI6Nq1oJECBAgAABAgQIECBAgAABAgQIEKiAgABoBTpBEwgQIECAAAECBAgQIECAAAECBAgQGBsBAdCxcVUrAQIECBAgQIAAAQIECBAgQIAAAQIVEBAArUAnaAIBAgQIECBAgAABAgQIECBAgAABAmMjIAA6Nq5qJUCAAAECBAgQIECAAAECBAgQIECgAgICoBXoBE0gQIAAAQIECBAgQIAAAQIECBAgQGBsBKaMTbVqJUCAAAECBAgQIECAAAECE1dg5cqI3/wmxxT9a+01I+69d6O49tqIXXeduM/syQgQINCrAkaA9mrPaTcBAgQIECBAgAABAgQIjLvAX/4S8apXRWyyScQBB8yo3f9rtdcZccMNJ8djHhOx7bYR731vxD33jHvT3JAAAQIEhhEwAnQYGKcJECBAgAABAgQIECBAgEApkCM+3/WuiNNPj8jjadMiDj44YscdI+bOjZg/P+L3v89RoREf/nDEZz4T8fGPR/zbv5U1eCdAgACBtSUgALq25N2XAAECBAgQIECAAAECBHpCYPHiiEMPjbj00tXBzhzh+aY3RcyePbT5t90WceKJtTGhZ6weKXrNNREf+cjQfM4QIECAwPgJmAI/ftbuRIAAAQIECBAgQIAAAQI9JtDfH/GKV6wOfu6xR8Qf/hDxznc2D37moz3qUatHf158ccRGG0WcckrERz/aYw+tuQQIEJhgAgKgE6xDPQ4BAgQIECBAgAABAgQIdE/gk5+M+O53V091v+yyiK22aq3u/fePuPDC2vZItWVCjzkm4qqrWisnFwECBAh0X0AAtPumaiRAgAABAgQIECBAgACBCSCwaFHECSdETKr95nzOORHz5o3uoR73uNUjQFetWh0EHV1puQkQIECgWwICoN2SVA8BAgQIECBAgAABAgQITCiBb30rYsGCiJe/PCKnv7eTjjwyYocdIi65JOKvf22nBmUIECBAoFMBAdBOBZUnQIAAAQIECBAgQIAAgQkp8L3vrX6sV76y/cebUtt6+GUvW12+rK/92pQkQIAAgXYEBEDbUVOGAAECBAgQIECAAAECBCa8wC9/GTF5csTTntbZox544OryV1zRWT1KEyBAgEB7ArX/i5IIECBAgAABAgQIECBAgACBUmDlyog77oi4++6IzTePWG+98kp777kzfKZ//GP1u68ECBAgML4CAqDj6+1uBAgQIECAAAECBAgQILCWBe69N+LWWyNuuWX1++DjG298uIG5AVKnqa9vdQ39/Z3WpDwBAgQItCMgANqOmjIECBAgQIAAAQIECBAgUEmBpUsHBjb/9reI+fMfDnTm5xzh2Wq6886Ihx5aPRW+1TKD89122+ozm202+IrPBAgQIDAeAgKg46HsHgQIECBAgAABAgQIECDQsUCOoBxu1Gaez13Wlyzp7DY5WnPLLSO22iri73+PyADo//t/Efvv3369P/nJ6rL77NN+HUoSIECAQPsCAqDt2ylJgAABAgQIECBAgAABAl0SyFGWCxYMnZpeTk/PkZu5JmcnKTc02njj1QHOMsiZ7+Vrp50iNtzw4Tt89rMRb3pTxFe/2n4AdNWqiG98Y3Wdhx76cN2OCBAgQGD8BARAx8/anQgQIECAAAECBAgQILDOCixcODS42Tia84YbIjpdI3ODDR4OZuYIzjKwWR5vt11EuR5nKx3x0pdGvPe9EV/+csQ73hGxyy6tlBqY56yzIv7859U7ye+668BrPhEgQIDA+AgIgI6Ps7sQIECAAAECBAgQIEBgwgosXz781PQcwZmjNx98sLPHnzZtdUCzDGYOHsG5444RU6d2do/BpefNWx0Afec7Iw4/POKKKyLmzBmca/jPV18d8ba3rQ66fuQjw+dzhQABAgTGVkAAdGx91U6AAAECBAgQIECAAIGeFsjgZuMmQo2jNvM4g5v339/5Iz7qUavX3Rw8ajM/P/rRows8dt6ah2s46qiISy6JuPDCiIMOivjOdyJa2czoV7+KeMELIhYvjjjhhIh99324TkcECBAgML4CAqDj6+1uBAgQIECAAAECBAgQqIxArk+Z62qW62zm++Dj/NxJmjQpYqONhk5HL0dw7rBDxCabdHKHsS2b64Z+85sRz33u6hGge+wR8YEPRLz2tc1HnKbnSSdFnH766lGvuYbo+98/tm1UOwECBAiMLCAAOrKPqwQIECBAgAABAgQIEOhZgRyZOTig2TiCM9fdzM2HOkk5JbzZqM1yqnquu5lBxF5Oc+dGXHZZxFvfGvGFL0T8+79HvOtdq0eE5tT7vJ6jZH//+4if/Sxi5cqIWbMiTj014s1v7uUn13YCBAhMDAEB0InRj56CAAECBAgQIECAAIF1TGDFiuHX3cwg5/XXRyxb1hnKeuuted3N6dM7u0evlM41SD/3uYg3vjHixBMjLrgg4tvfHtr6HM1abp7UylT5oTU4Q4AAAQLdFhAA7bao+ggQIECAAAECBAgQINChQAY317TuZu6q3knK4GYG68rRm+WU9PJzrruZu6pLAwX22ivi3HMjli6N+PGPl8Yhh7ytlmFGbL313Dj77A/E4x8fkdP+JQIECBCojoAAaHX6QksIECBAgAABAgQIEFgHBPr7I+65Z+Sp6Tff3BlEX1/Ehhs+HNwsp6M3Bjm33rqze6zrpWfMiDjggNoiqvH5gmLjjR8X++zzgXWdxfMTIECgkgICoJXsFo0iQIAAAQIECBAgQKBXBR54YODU9MFrcP7976vXiOzk+XJ9ycZgZjlqswx0br99xBS/7XVCrCwBAgQITCABfyVOoM70KAQIECBAgAABAgQIjK1AbhjUuInQ4ONcd3PJks7akBsGDQ5uloHNPJ+b7syc2dk9lCZAgAABAuuSgADoutTbnpUAAQIECBAgQIAAgWEFcufuf/5z6NT0cgRnBjdz6nonKUdlPuIRzaeml8HNnLouESBAgAABAt0TEADtnqWaCBAgQIAAAQIECBCosMC99w4NbjaO4Lzppohcn7OTNG9e8+BmOYJzm20icn1OiQABAgQIEBg/AQHQ8bN2JwIECBAgQIAAAQIExkggd+RuDGY2HucIzlx388EHO7t5bnoz0tT0XHdz6tTO7qE0AQIECBAg0H0BAdDum6qRAAECBIYRWLx4caxYsWKYq907vaS2+NqiRYti7ty53atUTQQIECCw1gSWL4/4xz9Wj94sp6Pne3mcwc377++sedOmRWy6aUQ5UnNwoDPX3Zw9u7N7KE2AAAECBAisHQEB0LXj7q4ECBBYJwUOO+ywuPDCC8ft2a+vLdY2Z86ccbufGxEgQIDA6AVyU6EFCx4OZjYGNsvj228ffb2NJSZNGn7dzTLgucUWjSUcEyBAgAABAhNJQAB0IvWmZyFAgAABAgQIECBQMYGFC0cObua6mxkE7SRtsMGa193MndUlAgQIECBAYN0UEABdN/vdUxMgQGCtCOy6667F1PRWb76w9lvztddeW2TftDYvcbvttmup6EO136TzNdlvuy15yUSAAIF2BXJqeuNam4OPb7ghYtmydmtfXS6npg+ejl6O2szzue7m9Omd3UNpAgQIECBAYGILCIBO7P71dAQIEKiUwKmnnjqq9lx66aVx4IEHFmVe/OIXx6c+9amWyucaoBk8lQgQIECgfYHcMOiuu0Zed7PTH7W5YVCuu5mBzPLVGNzcYYeIHN0pESBAgAABAgQ6ERAA7URPWQIECBAgQIAAAQI9KLBqVcTdd488Nf222yL6+9t/uL6+iI03bh7YbAxytn8HJQkQIECAAAECrQkIgLbmJBcBAgQIECBAgACBnhHIHdEbp6OXu6WXmwrdeGPEypWdPU7uMTd4anrj5223jZjit43OkJUmQIAAAQIEuiLgnyRdYVQJAQIECPSyQH8tEtD/vR/EoeedH0+K9aI2MCp2/MwZsWrxkug75F+iL+dnSgQIEKiIwIoVA4ObjYHOPM51N2srgXSU1ltvaHCzcdRmrrs5c2ZHt1CYAAECBAgQIDBuAgKg40btRgQIECBQNYH+310Vq455b/T/+LKiabsVXyetbubPfh6raq+Y1Bd9Lz08Jn3wA9HX4iZMqyvwlQABAqMXyFGZ5bqbjYHNcgRnBjfvuWf09TaWyFGZjetuNgY2cwRnBjc33LCxhGMCBAgQIECAQG8LCID2dv9pPQECBAi0KbDqxJNi1fs/UFvgrlbBdttG30teHH1Pf2r0bb551LaQj/5bbo3+Sy6N/nO+Hf3fODse+s73YtIZn41JR7yszTsqRoDAui6Q62lm8LIMZpbT0QcHOnN9znZTrruZwctmGwqVgc58z3wSAQIECBAgQGBdERAAXVd62nMSIECAQF3goTe+Jfo/d0bE7Fkx6ZQPR9/rXhN9gxaq69trz4hDD4n+kz4Yq045Nfo/+OFY9fJXR8z/Z0w66q31uhwQIECgFHjggeGnpmewM9fdzJ3VO0mzZo08NT3X3cyd1SUCBAgQIECAAIGHBQRAH7ZwRIAAAQLrgMCq0z65Ovj5yM1i8oU/iL7dHjviU/fVog2TP/D+WLXfE2PVC18Sq45+V8ROj45Jz332iOVcJEBgYgksWxZx552rR282G8GZU9MXL+7smadPj9hss4hypGbjhkLl1PTZszu7h9IECBAgQIAAgXVRQAB0Xex1z0yAAIF1VKC/FrVY9e73RUyfFpO/d+4ag5+NTJMOelbEl2obI73kiFh15Juj72/XRF9GKyQCBHpeoLbqRcyfP/LU9Ax+dpImT47YZJORp6Y/8pGd3EFZAgQIECBAgACB4QQEQIeTcZ4AAQIEJpzAqv/6cMSy5dH3vndF3xMeP+rnm3T4i6P/xedE/7e/G/2f+mz0veNto65DAQIExl/g3ntHDm7mGpwZBO0kzZs3cnAzR3VmEFQiQIAAAQIECBAYfwEB0PE3d0cCBAgQWAsC/StWRP/Z5xajPycd8462WzDp2PfFQ7UA6KqvfzMmCYC27agggW4JLF06/LqbGdi86aba/3vUpq93kmbMWPO6mwaEdyKsLAECBAgQIEBgbAUEQMfWV+0ECBAgUBGB/p9fHrFwUfQ97znRt/76bbeqb/fdInbYPuJ3v4/+O+5YvWt827UpSIDASAK5YdBI627mpkILF45Uw5qv5YZBOfW83DV98Lqb220XscEGa65HDgIECBAgQIAAgeoKCIBWt2+0jAABAgS6KXDNtatr23uvjmvt23vP6L/+79Ffq7Nv8807rk8FBNZFgVWrIv75z5Gnptf+jyH6+9vXmTQp4hGPeDi42Wxzofwj3NfX/j2UJECAAAECBAgQqL6AAGj1+0gLCRAgQKAbAvfeV9TSt2ltF5JO06abrq7h11dG/x67FxGWPhGUTlWVn2ACOTIzp6Dnjunlq/HzzTdHrFzZ2UPnyMzBIzYbP2+9dcQU/9rtDFlpAgQIECBAgMAEEPBPwgnQiR6BAAECBFoQKBfoW3R/C5nXkGXRoiLDqv/vAxH5mlwbZrbbY6Nv+9pc2dqreK9Nk8/3vozASAQmmMDy5QODm42BzXLdzSX/P3t3AmdJWd8L/1/dwywwwDDsywjDvogiIigiKKhoTExeIzdXMcnViN7XGBOUEG9ciLJETaIveBPuJcZojBgBP4rB4BZBlEXCJusgyL4O28wAM8zSXe/zVPfp0+f06Vl6zsycrv4+n0/NqapTVafq+0DPnF8/y9L1e+gZM8aGm6NbcM6fH7H55uv3Gc4mQIAAAQIECBCYGgIC0KlRz56SAAECBObvURmUv75nvS3GXGMg9eW96eYo05LLmB67228XxWGHprFD924NSXNAmgcgVAj0kECaLywee2xs1/TcijOHm3nczTyr+vqUzTaL2Gmnzl3TcwvOPO5mnlVdIUCAAAECBAgQINANAQFoNxRdgwABAgR6XqB47dGRW2qW//H9NKZgmcb8m9igf+XTT0dcdU3EFltE/NZvRNyT0qAcqj6V9o9Xnngyykt/mN794dhwdM7WURzy0mbL0dEtSM28Mp6o/RMUyONpPvnk2HBzdAvOhx5av3E38/9a223XOdxstODcbTfjbk6wCp1GgAABAgQIECAwAQEB6ATQnEKAAAECk0+gSIlM8Zqjorz8iii/9e0o3v62CT1E+Q//NyLN3lL8/juj/9wvjlyjzM3mbrm1mhwpB6JVK9HGa25Ot3KcwQ4XLa7uKfJ9jVxteGVm6gOcZp0f6Vo/3K0+d7OPHXaIor+//QzbU1zg2TTCw+gwc/R6bsGZx93MM6uvT9lyy9V3Tc+jPmjYvD7CziVAgAABAgQIEOi2gAC026KuR4AAAQI9K1B87C+qsHHwox+P4o2vj2KrrdbpXsv77ovBz/1dSnc2i74/P7nl3CL36U0zzBfjzDJf3rGgNRT9dZpFPs0kX7UezV3oO5UX0kCLeaKltOTSEpD2pWZ2Bx4wFI6OCkarcUf32qvT1eyb5ALLlkXkWdE7TSiUg870n2c899z6PeSsWRE77xzRaKk5ekKhvJ7H3Zw9e/0+w9kECBAgQIAAAQIENraAAHRji/s8AgQIENhkAn2vPy7K3/7NKC++JAbf8fvR9+0L13oMznLRohh46+9GPPtcFH/xkSjyIIXrUIoD9o+8dCrlU09F+cs0fmhuMTocijZakObP61gGUxx66+1RpiWXlnA079hpxyhS69GRSZly1/oUlFbbOeVSekogz4b++OOr75qe31+fkmdD33HH1XdNz+8rBAgQIECAAAECBOomIACtW416HgIECBBYrUDfV/8pBl75mjQW6A9i4Lg3Rf83/zWKXXZZ7TnlrbfFwO/+XsSv7o7iDcdF35mfXu3x6/pmse22URz7uoi8tJUyT6V98y0twehIOPrEE6k7/pjoc+gKjz0eZVpyGXPEVqkP83DX+qp7/egWpOlelO4LpIy7ark5ukv66PU87ubAwPp9bq663EqzvdVmozVnHnfTqAnrZ+xsAgQIECBAgACBySkgAJ2c9eauCRAgQGCCAkWaWKj/P78fA79zQsTPr4qBfQ6K4oP/b/Sd+I6hFpPD1y3TOJ9x9TUx+JWvRfnPX03pVBr3861vib6vf3Wjjr1ZbL55xCuPiCIt7aXMiVkKZxuBaPWaWpBWr/fd3354c3tJGigyPXuZl+beobXUvT9enEwarUXzeKONiZnStN1VV//2c6b49vPPjz/uZg4587iby9NoButT8pxb4wWbef8ee0TMSEPGKgQIECBAgAABAgQIjBUQgI41sYcAAQIEai6QW3z2//THMfhXp0d5zt9H+bnPx0BaIo8JultqDZr7Iz+UBlvMrS9z2WZO9H3iL6P4sz+Z8OzxQxfq7p/VJEgvfUkUaelUyocfjjJNzNSxa/3ycWbCWZEmc7rhpijTkktLQFqkHfvv15yUqQpH92qGpTVsXpiDyzWNu7lkSUU14T9ycJkbITdab7YHnfPnD/2nOeEPcCIBAgQIECBAgACBKS4gAJ3i/wF4fAIECExVgSKNg9n/2bOi/JMPRPm182Pw4n+Pgeuuj/7bF1Qkq9I01psd97oo0pihxe+fGMWcOZOOqth118hLp5LHNI007mhuLdregjTSzPQdS05D77gzyrTk0hKO5h3bbxfFwS8eajHa3oI0Tx3eYyU38l3TuJuPPZaec8yDrv2D9PWtedzN1LA2Betrf01HEiBAgAABAgQIECCwbgIC0HXzcjQBAgQI1EygSAMjFv/r1OhLy5mf/GScc/rpkedkP/cr/xz//R3vqNnTNh+nCnSPOTqKtLSXMjd7zOOOpnC0aj06anKmKjEcb9b6J56M8ieXR6RlTGa4RerKPzzuaKNLfTX+aG5FusMOG6Rl7TPPtHZNz7Onjx53M2/nxr7rU7bZZmzX9PYWnHnyIYUAAQIECBAgQIAAgU0n4J/km87eJxMgQIBAjwmUqbne0417msJN8orcJ/sVh0WRlvZSjY16x4KhcLQx3uhIK9Jfd2gWOnyF59NwAlf/Isq05NISkE7rjzjowNau9Y2JmXbeOar7Gb5M4yWPu5knDsohZnuw2Rh3c9myxtETe83jbuYGtI1JhNqDzTzuZmpIrBAgQIAAAQIECBAg0OMCAtAeryC3R4AAAQIEekmgyH26c1iZlk6lTH3Ky9R6tNFydOQ1haWxdJxEclWazOmXqcVpWnJpCUfT9jPb7hWPbbFX3Nu/VyxYvmfc9OzQ8uvYM1bExGb+2SzN9TR63M3RIWcj6MyzqisECBAgQIAAAQIECEx+AQHo5K9DT0CAAAECBHpGoNhxxyjesGPEG8beUvnss1XIueTGe+K5m++JVXf+OqY9cE9sufCe2GLZU2NPGN6zzVO/jrwckLZ/o+2oZ2JO3BwHxz0pDM1LDkXz65Lt94qtd58z7szpOfycwo182xRtEiBAgAABAgQIEKi3gAC03vXr6QgQIECAwEYVWJzmTxo9zubo9Qcf3DK9d2SsXHnkmHvaLLXlPChuj71ShLnXcJzZeN05Ho3NovNgndvEojgmflYtLRd9Im09NzOlnAelLvRpnNHZe0aRWpIWK9N6kZbBFNLWcNb6FgMbBAgQIECAAAECBAhUAgJQ/yEQIECAAAECayWQx9Rc3bibOex87rm1utSYg1bG9LgpDok7Zx0ydtzNeWXsV/wqXrTyntjp+Xtis4dSd/rREzPlLvSdyrIXIv7r+ijTkktL1/r+voj99xsad7Qx3miakGloYqYUlGoe2knUPgIECBAgQIAAAQKTUkAAOimrzU0TIECAAIHuCuTZ0B99tPOEQo2Jhp7IrSrXo+TZ0NOcRlW39MY4m/l19Pr223f6gCLt3G94Gft++WSaff6WW1tC0cYM9rHk2bEn5D15Jvvb7ogyLbm0hKN5x847RfHig6IxY331moLS6nXzNKO9QoAAAQIECBAgQIDApBEQgE6aqnKjBAgQIEBgYgJlSvdSRthxtvRGuPnww6lXeMoEJ1pyg8nttlt9uJlnVM9zKHW7FOmDi9e9NiIvbaXM08XfeluUeRKm4dnqR8LRhQvTQ4+JPoeu8OhjUaYlfvSfY8PRrbeKSOFobi1atRgd3YI0IygECBAgQIAAAQIECPSUgAC0p6rDzRAgQIBAFshh3SWXRPzbv+2ftn6Ulmnxgx9sFX/91xFvfWuahDw1zFOaAmluoXHH3WyMwbl8efP4iaxtuWVrS83RrTYb6zMmNiH7RG5nrc8pttgi4ojDo0hLeylzs9fbUyvQFIw2wtFIQWm1/cgjEctXtJ8ytL14ScSVV0eZl/YjZkyPOPCAKBqtRVNIOtKKNDV/LfL08woBAgQIECBAgAABAhtVoHYB6PL0De+iiy6K6667Lp555pnYZ5994pBDDok3velNaa6D/nXCveyyy+Lmm28e95ztUiuPE088ccz7CxYsiAsvvDDuv//+2CJ98Tr44IPj2GOPjT33TF+CFAIECBAYVyCPL/nJT0b8y7+kHsrVsI5pqu7IS8qnUgO+v/zLoeXwlGV99rMRr31t9Vat/8jB5ZrG3VyS8rj1KTm43G23ZuvNRqCZX/Oy++4RW6VGj3UrRe6T/5KDo0hLp1LefXczHB3VgjSHpfHCOIlyDk1v/GWUacmlJSDtS81k991neJzR3Hp0uEt9IyzN96MQIECAAAECBAgQINB1gVr9S3vRokXxgQ98IHXxe7CCmjt3bnz/+9+vlquuuipOO+20mD49tcxYy3LxxRfH9dcPTZzQ6ZQcaLYHoDl8Pfvss6vDZ8+eHStWrIgbbrghLrjggvjMZz4Thx56aKdL2UeAAIEpL/Dd70b6mTo0ic6220accELE8ccPhW8z02TeeXzKK6+M9PM04tprU2/n10X86Z9G/N3fxaSdzDuHvI+lXtb5r61GS8329ccfX7//NPLv/nbaqRluvuhFY9d3TBOiK2MFir33Ti059x77RtpTpl+yRhp3NLcWbW9BGs8s6nhO1d1+wa+iTEsuLeFo3rHD9mnS+gOHWow2QtFGC9I6JtD5mRUCBAgQIECAAAECG0GgVgHo6aefXoWfRxxxRHziE5+IrbfeOh5Og5p97GMfiyuuuCLOOeecOOWUU9aa9a677qqO/dCHPhQzOvTr2zL3BxxVbrnlluozcsiaw9bXvOY1sSp1r/vOd74z8tnnn39++iKavokqBAgQIDAi8JWvRPzRH6VAKCVC+cd0+hE+psXhAQdEak0f8fGP567xEelHc/qF01BwmH73tEHGlhy5wQmuPPVUa7DZHm7mcTdzL+z1KTksbm+xOXo7j7upYeH6CHc+t9hmm4ijXxNFWtpL+UKafT6POzqqa31jDNIq8c4TMHUqC5+IcuFPIy776dhwdHbqyj887mijS301/mgOSFOCbdb6TqD2ESBAgAABAgQIEBgSqE0Aevvtt6cWQdfGrFmz4owzzoiZublQKrumb36f//zn421ve1tceuml8f73vz/ag8shitY/F6aJEZakPoXbpm+WJ+RmSGtRvvrVr6Yv72W8613viqOPPro6Y7M01lc+/5E0llhuHZrD0P/5P//nWlzNIQQIEJgaArlVZ/rRXLXi/MY3In73d1f/3HmynXe8I+KVrxxqIfrtb0f6RVdU44Ou/szuvvvcc0MtN3Oo2R5sNlpzLlu2fp+ZOhJUXdM7tdpshJzprz2lxwSK/G+Qw14eRVraS5mb/d6ZWoHmcDR1q2+GpL+uxiEdd1Km59JkTtdcG2VacmlpPbpZ+ufcAfuP6lqfQtHGxEy77BJFh1/itt+XbQIECBAgQIAAAQJ1FqhNAHr55ZdX9XTMMceMhJ+Nistd4Q9PA8ZdffXVVQj63/7bf2u8Ne5ro/XnfvvtN+4xo99YunRpFcDmfcfnPpttJe/LAeglaVaP9773vak1Tm3o257UJgECBNZeIGdB73tfpOFCIv7P/1lz+Dn6yvPnR3zve5F+vkd87nMR73xnpDGXRx+xPuvTY+HCLVLvgfFbcOYe0OtT8ogsuXVmDjIbYWZjvbGdGxkq9RIo8pgEeZKktHQqZRoToUytR0cmZWp0sU9haTy/tNMpEStTM+KbU3f8tOTSEo6mXxhEGmt0zGz1ja71w78wrk70BwECBAgQIECAAIGaCtQmhbvttvRlIZXc/b1TaQSgeVKjiQSguSt7bhGaw9RO5Y470iyyqfXnvPStdZfU2qK97L///lXL08WLF6dx3h4wIVI7kG0CBKakwL/+a5qE+/aI9LurqhXouiKkee7i058e6g6fu8anoZvXWAZT7+M8rmanVptXX/3edH5qjho7pfFFc3I0sdLXV/VK7hhuNlpz5tFQcmtWhcBogSL9h5GXeP1xo3dX62We7Wq4a317C9J48sm25HP49JyG5pamOUD9wY9aw9F8yNyUsg93rW90qR8JSyXww4heCBAgQIAAAQIEJrtAbQLQPNZnLnPmzOlYJ439jQmSOh40amejBWgOPk8++eS48cYb04zEA1WI+YpXvCKNPfehqnt845Q1fX4+Lt/Ds88+W41TOt6M8DlkfXycGS/yubnk+1i5cmW17g8CBAhMZoF//dfUGi76qpnfJ/oceVSR008vq9agTzyxqhpH9O67y7j77hlp+JH+9DN1WloGq8DzoYeKakb1VavGSx53W6vbmDMn/8Irt9wsUxf15nrel7fzjOppBJTVlvUd+3O1F/dmPQXyeAevOGxoaX/C3Iz69gWp5WgKOu+5d2jJ3ezz+iOPRjHef3BPp6bMV/w8yry0XbPMn5e61sde8yP95raanGlkfac0c1ZuzaoQIEBgiguM/l6WG8SM3l4dTf6e2Sjrcl7jHK8ECBCYSgKNn6355+VES20C0OefT2NjpdIIOttBthqePbVxXPv77dt33313tetrX/ta+vd9f+QWnPkvqXvuuSd+8pOfxHXXXVfN9r738OywjeuO9/n5YmtzD3nW+jyBU6eyzXBLjByEPplbeigECBCYxAJp5JD46U93Si3ry9QCdLxAcs0PmIPG3/qtIr785dylvD998UjNLyOnj2nSmAmV3M34ofRzf/N46Uvnplb9A+m6A8Ovg9X65puv/i/e1NhfIbDxBXZLPVDycsxrWj87/UOxP4Wh/fc/EH333V+99ufX++5LywPjhqNFHsT2hhuHltYrRpmaOQ+kbvQDe+zesgzu/qIY2HnniFlpHFSFAAECU0AgD4XWKPn74rp+T/teGs+n0ZimcZ0N+XrSSSeZOG9DArs2AQIbRGD58uXVdad8ADqY+jO+kGdcTWW8CY5m55kkUmmgVRvj/JHDzDxpUS557M6PfOQj1eRKeTvv/9SnPpW6bN4eZ511Vpx33nnVeJ6Nv/jG+/x8buMeGvea9ykECBCYqgL33DMthZVFChnXvyHZoYdGFYAOhZ/ji/b3l7HTTjnQHGwLNocCzm9/+5w499wzqgt86EP/EL/zO78z/sW8Q2CyCKSxFgbSpEh56VSKp56KaXfelcLQZijad39eT4HpcO+T9vOK9G+vaXfdXS3t7+XtgXm7DQWju+eANIWio4LS2HzzTqfYR4AAgZ4QyI1d8rK2ZXRLzhxkfjyPybMO5aabboobbrhhHc5Yv0Pf8573mI9i/QidTYDAJBWoRQvQvtQKIc/+viy1VBgv4Gzsn55nnVhDydc6//zzq9/eHXLIIS2/Icvje346DTh34oknRu4mn2eeP/LII2OLLYZaGq3IXdDGKY17mLGa2Vh3S/0m3/CGN3S8Qr73/BvCPLN8Y5b7jgfaSYAAgUkg8MILQ38F7Zh60q5vaVxj5swytdwcCjN32WVVFXLmbul53M0ceu60Uxl5fM6xJbdAnRY//nHzZ7iftWOV7KmpQJ6NKy+pDAwv1Ub+I0281Lfgzui799603JeW+6NILUfzerHwiSjG6YbU/+BDkZf42ZUjl2qslKlXzuD++8bgHnvE4PzdY3DP+dV6mdbL7bZrHOaVAAECm0Qgzxnx5dytZALlqfQLpXU998UvfvEEPmnip+TvkSbknbifMwkQ2DQCRRcmT6hFAJr5t0v/YM7jezbGyWyvksb+RlDZ/v7o7Ryo5smM8tKp7Ji+aR+cphrO3eBzl/gcgObPzyWP4TleWZt7yNfKS6eS/0LNAWh+hkZ3+E7H2UeAAIHJILDDDkN3uZofm2v9GI0u53/yJ0WaEX5aLF26IhYvHvp5nFvmN1rgr+mC+RdgjZLP8bO2oeF1ygrk4Xd2GwpH2w3KPB55CkerCZlSF/uWiZny2OzLm79QGH1ukf6n77/2umoZvb9anzkjYv/9ositVVMX+5aJmdIvoYs1Da475oJ2ECBAYN0ERv9bYN3OnNjRb3nLW+Ld7373SIOaNV0lT/77xS9+sTrs2GOPXasJfkdfM39v7UaQMPqa1gkQILChBRoNCnNeN9EyZQLQRjDZrS+zOwx/c8+hZC6NALQRcnaqkG7fQ6fPsI8AAQKTRWB+mlcll+Ehl4c2Jvhn4xqNa07wMk4jQGAdBKow8uAXR5GWTqVMEzKNhKN5FvpGSJpeY9nQ0EVjznshje90081RpiWXltF2+1JL7X32Hg5F92qGo42wVDg6htMOAgTWXeAP/uAP4tWvfvW6n7iOZ+Su84vTb3BfmsYCyg1s5s6du1ZXyHNGNALQl7zkJfH+979/rc5zEAECBKa6QG0C0EYgmVtkvvKVrxxTr3l/LgcccMCY99p33HnnnXHNNdekIao2jxNOOKH97Wp74cKF1Wvusp5L4/NzK9Q8O1XuOjm65L/cnn766dT1si/22Wef0W9ZJ0CAwJQUyL9HSo3p45ZbIg0pknKN9fjRmBrHV+X1r5+SlB6aQE8KFHvlkLLzuKNl+jdR3HZ7FZCOhKQ5GM1BaZ6ZvlMZTHFoGqu0TEsuLeFo3rHjDlFUs9anlqONUDS1Iq1akm69dT5CIUCAwBoF9txzz8jLhi75O+O6Tpi0oe/J9QkQIFBngdoEoMcdd1z84Ac/SOO3/Tje+c53ttRZniSpMZB1HtNzTeWZZ56JL33pS1XXgMMPPzx2TwP4jy45yMxdD3I56KCDqtc8NmieKX7BggXxi1/8Io466qhqf+OPyy67LAYGBuLAAw+sgtXGfq8ECBCYygJve9tQAHrOOZFaM0xMIv14rULUPITW+oSoE/t0ZxEgMBGBIrd0es1RUaSlvZR59vnb72i2Hs0tR4dbkMajj6aBSgfbTxnafnxhlGmJy68YG45umSbDPPCAZqvRUd3rU9OrKNajO1Xnm7GXAAECBAgQIECglwRqE4DmVp97pMH088REl156abz5zW8ecf76178euat6DjKPOOKIkf155corr0xjxS2NvffeO+YP953MIemcOXNi0aJF8ZWvfCU+9rGPjQwUnWdw/+xnP1tNuJS7RuTQs1He8Y53xGmnnRb//M//XHVlaMwIn1uLfuMb36gOG69FaeMaXgkQIDCVBD74wYgvfCHi//7fiPe+N6oZ4dfl+dOP5Pjwh4fOWMdJV9flYxxLgMBGFCjyWLwvPzSKtLSXMv0yOX6VWoEOtxYdaj063NU+T7o0Xtf6Z5+L+MV/RZmWXFpaj05PvXb227clHI3UgrQafzRNDlWsZvLK9vuzTYAAAQIECBAg0JsCtQlA80DOJ510Unzyk5+Ms846K66++uqqq/ktqW9lXs9d0k899dQxAz6fffbZqTHBo9W5jQA0z4yXg8wPp2/VuUXp9ddfH2984xujv78/rrjiinjooYeqsPTkk09uqdVjjjmm6mJ/xx13pC/y743Xve51kcd2ydfIAWwOTPNA1QoBAgQIDAnk+eP+6q+GQszf/u2Iq66KNHP72unkHOSP/igNF3hTakj2mkiTAKzdeY4iQGDyChTp32KRurlXXd07PEZ5330tLUdHxh3NLUife77DGWnXijSZ0y23RZmWXFrC0TTsaOoLOxSGNkLRUa1Hq7C2OssfBAgQIECAAAECvSxQmwA0Ix999NGpJdEXqgA0dznPSy65ZWgOK/Mg0WtbDjvssDj33HMjB6Q50PzmN79ZnZpnBTz++OOrcDSPETq65IA0D0id7+GHP/xh5JanueT9b3/726sBqtdnxqrRn2WdAAECdRHIv0u67rqI88+PSKOOxAUXRBx55Oqf7oknItIcBZHmAYh58yIuvDDSL7hWf453CRCov0CR/s2Xlzhu7C+cyzQee0vX+hSKNsYfjfxDpSX5HLbK+3IX/Nzi9Ic/HnvItqkr/3DX+pYZ63NYupYTmgx/khcCBAgQIECAAIENKFCrADQ7vexlL0tfhC+sWlzmCYny5EQ77bRTNflQJ8cL8jftcUoe3/O8886rZufL18pd2uelb9qrCzFnpG5SH/3oR+OUU06JX+fZT8uyOmeLLbYY51PsJkCAAIE0ckjkXqb5NQ+hnOefS436IzWsTy34mz55+OX8+6j0u6lYsiSPwxzx3e+muU92bB5jjQABAp0EijwR0qteGUVa2ku5PM0+v+DOkbFGG8Fo9frwwxErV7WfMrT9VJrM6WdXRpmX9iM2T135c2vVxkRMo1qQxs47R9Watf0c2wQIECBAgAABAhtEoHYBaENp2223jbx0o2yd/sGcl3Up06ZNi/32229dTnEsAQIEpqzA9OkRX/7yUFf2NOxy1Qo0/35q+vQydt21qMLRRx4pU+g51Mwzh6J/8icRZ54Z6ZdTU5bNgxMg0CWBapzPl74kirS0lzJNpplnpx8KRRuvoyZmyl3oO5WlaTKn62+MMi25tASk01JX/n32butaPzzu6LzdQtf6TqD2ESBAgAABAgQmLlDbAHTiJM4kQIAAgU0l8O53R/ze70WaOC7iO9+JNJTJQNx779BfVX19A3HYYdPirW+NeNe7Io3FvKnu0ucSIDCVBKoZ4vfdJ4q0dCplmuwy7lhQBaSN2eobLUhjUep236msSoMY35FanKYll5ZwNO/YY/dmy9FRLUgjjz86O81orxAgQIAAAQIECKyTgAB0nbgcTIAAAQIbWiAPr5wnN8rLT35yRRx33O+kj5wW73vfO9PYzP97Q3+86xMgQGCdBIo03FIacymKY44ec175XJp9PoejeRKm4bFER8LRxx6LGBwTfQ5d4777o0xL/OdlY8PROalX0nhd67fffsyEn2Nuyg4CBAgQIECAwBQUEIBOwUr3yAQIEJhcAs9Wt9vXN05QMLkext0SIDCFBKrWmq84LIq0tJdyZeo6f+evRiZiqoLRRlf7hx6KWL6i/ZSh7dyq9OpfRJmWXFp+Ms5Mgynvt28UabzRqrVoo/Voft111yhGD6o8dDV/EiBAgAABAgSmhIAAdEpUs4ckQIAAAQIECBDoJYEqjHzxQVGkpb3kSTTT+B/NcHRUC9LckjTy+KKdygtpMqdf3hJlWnJpCUf7+0aFosPjjeYu9TksfdG8KEzY2UnUPgIECBAgQKAmAgLQmlSkxyBAgAABAgQIEKiHQFGkCd/2TOFkWuINY5+pfOqp5rijORAd7l6fJ2uKPDN9pzKQJnP61d1RpiWXlnA079ht15GAdEwL0jlz8hEKAQIECBAgQGDSCghAJ23VuXECBAgQIECAAIGpKFBsu23EUa+OIi3tpVy6NGJBmmCpLRitth95JCIHoZ3KQw9HpKX86c/GhqNbbRmx/34tEzMVw93rY6edopooqtM17SNAgAABAgQI9IiAALRHKsJtECBAgAABAgQIEFhfgSLPJHfoy6JIS3spV62K3Eq0ZbzRXw9vP/BgxLIX2k8Z2l6SxmK+9roo05JLS+vR6ZtF7LtPSzgaqVt9FZDOS13rp08fuoY/CRAgQIAAAQKbUEAAugnxfTQBAgQIECBAgACBjSVQTEv/9M8tOdPSqZT3p9nnR7ccHd21/tk0o32nsiJN5nTr7VGmJZeWcLQvdeXfY4+hMLQRiuZxR4dbj1Zhbadr2keAAAECBAgQ6LKAALTLoC5HgAABAgQIECBAYDIKFLvvHnmJY1835vbLRYtau9Y3WpLmwHThwrbkc/j0wRSH3pMmc0pL/Og/W8PRfMh2qSv/cNf6kVA0h6M5LM3d/BUCBAgQIECAQJcEBKBdgnQZAgQIECBAgAABAnUVKPJESK88Ioq0tJfyhdR1/ld3Rdk2W33VmvShhyJWpq73ncqTaTKnn18VZV7a398ideXfb99ma9EUikaj9eguu0TR399+hm0CBAgQIECAwLgCAtBxabxBgAABAgQIECBAgMCaBIqZMyNecnAUaWkv5WCadKnqSp9mqG90qc+vOSx9II07+tzz7acMbT+fJnO64aYo05JLS0C6WfoK0whDR7rWD487uvuLorqfoav4kwABAgQIECBQCQhA/YdAgAABAgQIECBAgMAGEahmiN9n7yjS0qmUD6fZ54eD0fYWpPFM6nbfqeQWpQt+FWVacmkJR9Owo/GiFII2xhltvDZakG6ZZrRXCBAgQIAAgSknIACdclXugQkQIECAAAECBAjgv2/pAABAAElEQVT0hkCx664RaSmOfs2YGyqfTbPP35mCzlFd6xuTNMWjj0bkMUbbS951/wNRpiV+cnlrOJqP3SZ15W/rWt8IS2OHHaIocoKqECBAgAABAnUTEIDWrUY9DwECBAgQIECAAIEaCBS5teZhL48iLe2lXLEi4q67W2etb0zM9GDqWr88vd+p5Fal11wbZVpyaYlQZ6Wu/Lm1aqO1aKP1aH6dNy+Kab46dSK1jwABAgQITAYBf4tPhlpyjwQIECBAgAABAgQIjAgU06dHHHRgFGlpL2WZYs377muGo6NakObu9pHHF+1UlqXJnG6+Ncq05NISjvb3Rew5P3WtHzUZUwpGq7A0jzu6xRadrmgfAQIECBAg0CMCAtAeqQi3QYAAAQIECBAgQIDA+gtU3djnp7AyLfH648ZcsHzyyaGu9aMnZcrBaA5K88z0ncpAmszprl9HmZZcWsLRvGPXXUYmZhrTgnSbbfIRCgECBAgQILAJBQSgmxDfRxMgQIAAAQIECBAgsHEFiu22i0hL8eojx3xw+Xyalf5XdzVbjw4Ho9XYo3nCphyEdioPPxKRlvKKn48NR7dKXflHjzuaW44Od6+PnXeOaqKoTte0jwABAgQIEOiagAC0a5QuRIAAAQIECBAgQIDAZBaourK/7JAo0tJeylVp9vlGq9FqvNHUGrSxnSddyl3oO5UlaTKn/7o+yrTk0tJ6dEbqyp/GHG0EotVrYzvPZp+7+isECBAgQIDAegsIQNeb0AUIECBAgAABAgQIEKi7QDUJUm7JmZb2Uo07miZfasxS3/Kau9bnELRTyZM13XZHlGnJpSUc7Usz0u+++1A42ghFG61H5+8R1SRR1Vn+IECAAAECBNYkIABdk5D3CRAgQIAAAQIECBAgsBqBatzR3GIzLfG61445snzmmdau9VUL0jTuaO5i//jjbcnn8OmDKQ69974o0xI//klrOJoP2XGHkdajY1qQ5m7+CgECBAgQIDAiIAAdobBCgAABAgQIECBAgACB7gsUeSKkIw6PIi3tpXwhdZ2/6+4oc0vRRpf6xmtqVRorU9f7TuXxhSk8XRjllVePDUdnp1np99m72bU+tSCNRuvRXXeNor+/0xXtI0CAAAECtRUQgNa2aj0YAQIECBAgQIAAAQK9LlDMnBlx8IujSEt7KQcGUivQe8d2rc9h6X33Rzy/tP2Uoe3n0mRON/4yyrTk0tK1frP0FXDP+aO61o8KR/dIXe7z/SgECBAgQKBmAgLQmlWoxyFAgAABAgQIECBAoB4CVUvNvVNLzrR0KuUjafb54dai7S1I4+nU7b5TyS1K70wz3acll5ZwNA07GvPmNVuODs9WXzRakG61VXWOPwgQIECAwGQTEIBOthpzvwQIECBAgAABAgQIEEgCxS67RKSleM1RYzzKJUvGdK1vTM4UOTjNY4y2l7zrgTSZU1risp+2hqP52LmpK39b1/rG+KOx445RjYXafk3bBAgQIECgBwQEoD1QCW6BAAECBAgQIECAAAEC3RQocmvNlx8aRVraS7l8+UjL0UYL0qgmZkpd63P4mWen71Ryq9Jf/FeUacmlJUKdlbrOp5aiI61FG61H82ueIGqar56dSO0jQIAAgY0j4G+hjePsUwgQIECAAAECBAgQINATAsWMGREHHhBFWtpLOTiYQtAHmuOOjp6cKc9Iv+TZ9lOGtpelyZxuuS3KtOTSEo5OS5Mu7bFH567189P+zTevzvEHAQIECBDYUAIC0A0l67oECBAgQIAAAQIECBCYZAJFX99QWJkCyzju2DF3Xy5Ms89XrUXvaYakaRzSvC+eeHLM8dWOVWkyp3xOPiaVlnA079hl55FZ6se0IJ07Nx+hECBAgACB9RIQgK4Xn5MJECBAgAABAgQIECAwdQSKHXaISEtx5KvGPHT5fJp9/q67xwSj1dijDz0UMZBal3YqjzwakZbyZ1eODUe3Tl35R487mrrUj4w7uuuuU2rc0XLFitgqTV7129EX+Yv8ng8+HOUzz0SxTRqbVSFAgACB1QoIQFfL400CBAgQIECAAAECBAgQWBuBYostIg55aRRpaS/lypURqQt9FYbe3RaS3nd/RO5C36ksTpM5XXdDlGnJpaX16IzpIy1HoxGM5nFI99orYvc07uj09H4NSnnNL2LwC+dEeekP4vBnn4uLYrOhp/rWd2PgO5dUk2AV7/nDKE58R1QteGvwzB6BAAEC3RYQgHZb1PUIECBAgAABAgQIECBAoEWg2CyFdvvuE0VaIo5vea8sU6yZWog2Zqlvec3d5nMI2qnkyZpuXxBlWnJpCUf7iqHJl/IkTFUoml4bIeme86PYcstOV+ypfeXixTH4vg9EecG3hu5r81lRHP+G9FzzIvI4ro88EuVV10R5+RXVEn/3/0X/v3w5ipcc3FPP4WYIECDQCwIC0F6oBfdAgAABAgQIECBAgACBKSpQFCmsnDcvirTEa48Zo5C7ebd0rc/jieZxR/Py2GNtyefw6YMpDk0tS8vcuvQ/L2sNR/MhO2w/Eow2utRXrzks3T69t4lLee+9MfAbvx2x4FcRu+0afZ/6RBTv+L0oZs1qubMcHpeX/zQGP/5XESkMHXj1a6Pv3/41+t7y5pbjbBAgQGCqCwhAp/p/AZ6fAAECBAgQIECAAAECPSxQjXF5+CuiSEt7KZctq4LQaoKlFIg2gtHq9f4UfuYJmDqVhU9EpKVqQdn+/uzUlX90q9G0PtJ6dLfdouhPs9pvwFIuWRIDv/n/VOFn8Ztvjr7z/2XcFqs5PC5el0LPKy+PwTM/E4Of+KsYPCF1hb/iP6M47OUb8C5dmgABApNLQAA6uerL3RIgQIAAAQIECBAgQIDAsEDVIvLFB0WRlvZSDqTw877hcUdHh6O5W30ajzSeX9p+ytD2c2kyp5tujjItubR0rd8sfYWev8fQREx77z0yIVPVejTvnzkzn7JeZfCP/7Tq2l/8xvHRd/G31npcz76PfTQitRAd/MhfxMDv/vfov/OWrtzPej2MkwkQINAjAgLQHqkIt0GAAIGpIPCtb30r7k1duta23J0mSWiUG2+8Mf72b/+2sbna1xVpltQXXnghTjrppNUe500CBAgQIECgvgJVS800IVI1KVKHxywfTbPPDwej7S1I46mnO5yRdq1cFfGrNIlTWiJ+2BqOpp78kVuI5nFHG+ON5tfcgjSPOzpnTudrjtpb3nhTlF//RsSOOwy1/OzrG/Xumlf7PvynUV7xsygvviTKs/93FH9xyppPcgQBAgSmgIAAdApUskckQIBArwj84z/+Y/zgBz+Y0O1cffXVkZd1KX/4h3+4Loc7lgABAgQIEJhCAsXOO0ekpTjq1WOeOk9AVIWjubXo6NajaT0efjgijzHaXvKuB9NkTmmJPDFR+/vbzm0Go3vsETN23D4Gdt99aGKouem9VAa/+A9Vk9O+T/5lFFtv3X6Ftdru+9xfx8C/f6+6VnHqR6IaY3WtznQQAQIE6isgAK1v3XoyAgQITHmB17/+9bFy5croW8vWE4vzl53h8sd//Mdx6qmnNjbX+HrwwQfH9773vTUe5wACBAgQIECg9wWq8PHQl0WRlvZSLl8ecc+9reONpl4r1bijedKlFSvbTxnazq1K01Jee13kxqJbjTpqVZrhPebvkSZ7SoHrtGlRnPiOUe+u22qx7z4RR74q4udXRVx/Q4SxQNcN0NEECNRSQABay2r1UAQIEOhNgZNPPjlOOOGEDX5zy9MXk2VpUoSPf/zjVVf4iXzg008/HXlZ27J9D8wYu7b36jgCBAgQIEBg4gLFjBkRB+wfRVraSzk4mFqBPtgMR3ML0hyWDrckjWefaz9laHtpmszptjuG1l91xIRbfzYu3vf6Y2MwBaB5kieTITVUvBIgMJUFBKBTufY9OwECBDaywPHHH79RPnHp0qWRW3OeeeaZ1edtjK5fM7sw6cFGwfEhBAgQIECAwAYTKHKvk9ytPS1x7OvGfE75RJp9PoWhq+68K5beemv03/dATHvgwZiWZ6zPM9OnUszbbcx567xj+Bpl7q6vECBAgEAIQP1HQIAAAQK1Fbjttttiyy23jNmzZ9f2GT0YAQIECBAgMHkEitxjJC+pW/rSJ5+sbnxGalE6N40BOnhJGrfzt3636gK/3k+UutFXpRwzEul6X9oFCBAgMBkF1m1Kucn4hO6ZAAECBAgQIECAAAECBAj0uEDVajTdY/nwI+t/p8PXKHbYYf2v5QoECBCogYAAtAaV6BEIECBAgAABAgQIECBAYJIL7LdvxJap18p/XRdlGst8fUp5xc+r04tXvHx9LuNcAgQI1EZAAFqbqvQgBAgQIECAAAECBAgQIDBZBYrp06N4cxovPU2IVH7nuxN+jPKxx6L8yWURc7eJOOrVE76OEwkQIFAnAQFonWrTsxAgQIAAAQIECBAgQIDApBUoTnpPde+Dp306ypUrJ/Qcg6edHrFiZRR/9D+i6O+f0DWcRIAAgboJCEDrVqOehwABAgQIECBAgAABAgQmpUDf64+L4o2vj7jr1zH4wT9b52cY/Na3o/zHf4rYZk70/a9T1/l8JxAgQKCuAgLQutas5yJAgAABAgQIECBAgACBSSfQ90//J2KnHaM8759i4M8+EuXAwFo9w+AFF8Xg7787oiii71++HMU2qQu8QoAAAQKVgADUfwgECBAgQIAAAQIECBAgQKBHBIrddov+734rYrttozz772PgqNdF+fMrx7278oEHYuB/vDcGf+9dEcuXR98XvxB9v/kb4x7vDQIECExFgWlT8aE9MwECBAgQIECAAAECBAgQ6FWB4hWHRf+1P4+BE94Zcc21MfCa4yL23TuK498YxYvmRcyYHuUjj6Zg9KqIq66OGCwjtt8u+r7ypej7jTf16mO5LwIECGwyAQHoJqP3wQQIECBAgAABAgQIECBAoLNAMX9+CkGvjPLr34jBz58dcdPNUf7q7khRZ2vZdZco3vOH0XfKyVFstVXre7YIECBAoBLQBd5/CAQIECBAgAABAgQIECBAoAcFir6+6Pv9E2PajddG/313xq1/fnL8ZayKT6TlouOOif7rror+B38d/Z8+TfjZg/XnlggQ6B0BAWjv1IU7IUCAAAECBAgQIECAAAECHQWK3XePx449Jv4mBuIzafnFwQdG8fJD05xHRcfj7SRAgACBpoAAtGlhjQABAgQIECBAgAABAgQIECBAgACBmgkIQGtWoR6HAAECBAgQIECAAAECBAgQIECAAIGmgAC0aWGNAAECBAgQIECAAAECBAgQIECAAIGaCQhAa1ahHocAAQIECBAgQIAAAQIECBAgQIAAgaaAALRpYY0AAQIECBAgQIAAAQIECBAgQIAAgZoJCEBrVqEehwABAgQIECBAgAABAgQIECBAgACBpoAAtGlhjQABAgQIECBAgAABAgQIECBAgACBmgkIQGtWoR6HAAECBAgQIECAAAECBAgQIECAAIGmgAC0aWGNAAECBAgQIECAAAECBAgQIECAAIGaCQhAa1ahHocAAQIECBAgQIAAAQIECBAgQIAAgaaAALRpYY0AAQIECBAgQIAAAQIECBAgQIAAgZoJCEBrVqEehwABAgQIECBAgAABAgQIECBAgACBpoAAtGlhjQABAgQIECBAgAABAgQIECBAgACBmgkIQGtWoR6HAAECBAgQIECAAAECBAgQIECAAIGmgAC0aWGNAAECBAgQIECAAAECBAgQIECAAIGaCQhAa1ahHocAAQIECBAgQIAAAQIECBAgQIAAgaaAALRpYY0AAQIECBAgQIAAAQIECBAgQIAAgZoJCEBrVqEehwABAgQIECBAgAABAgQIECBAgACBpoAAtGlhjQABAgQIECBAgAABAgQIECBAgACBmgkIQGtWoR6HAAECBAgQIECAAAECBAgQIECAAIGmgAC0aWGNAAECBAgQIECAAAECBAgQIECAAIGaCQhAa1ahHocAAQIECBAgQIAAAQIECBAgQIAAgaaAALRpYY0AAQIECBAgQIAAAQIECBAgQIAAgZoJCEBrVqEehwABAgQIECBAgAABAgQIECBAgACBpoAAtGlhjQABAgQIECBAgAABAgQIECBAgACBmgkIQGtWoR6HAAECBAgQIECAAAECBAgQIECAAIGmgAC0aWGNAAECBAgQIECAAAECBAgQIECAAIGaCQhAa1ahHocAAQIECBAgQIAAAQIECBAgQIAAgabAtOaqtckisGTJknjiiScmy+26TwIECGx0gcHBwZHPfP7552PZsmUj21YIECBAgAABAptaoCzLkVtYsWLFWn+/W7Ro0ch5S5cuXevzRk6yQoAAgUkosHz58uquR3/PW9fHEICuq1gPHD9t2rTYbLPNeuBO3AIBAgR6U2DVqlXR+Muxr6/Pz8zerCZ3RYAAAQIEpqxA/nfKwMBA9fxFUaz1v1Xyd8FG8W+choRXAgTqLtD4brc+z9n86bk+V3HuRhXYfPPNY86cORv1M30YAQIEJpNAbhGxePHi6pZnzZoVs2fPnky3714JECBAgACBmgusXLkyGi2acuOWtf1+N/rfNDNnzlzr82rO6fEIEKi5QOPnZf7Fz0TLxM+c6Cc6jwABAgQIECBAgAABAgQIECBAgAABAhtJQAC6kaB9DAECBAgQIECAAAECBAgQIECAAAECG19AALrxzX0iAQIECBAgQIAAAQIECBAgQIAAAQIbScAYoBsJ2scQIECAAAECBAgQIECAAIHRArfeemvkscu32mqr0bvHXf/lL3858t6DDz4Yl1122cj22qy89rWvjTzpkkKAAIGpJiAAnWo17nkJECBAgAABAgQIECBAoCcEPvWpT8WVV145oXv51re+FXlZl5InXxo9k/y6nOtYAgQITGYBXeAnc+25dwIECBAgQIAAAQIECBAgQIAAAQIEViugBehqebxJgAABAgQIECBAgAABAgQ2jMAb3/jG2HfffWPmzJkb5gPartrXpw1UG4lNAgSmiIAAdIpUtMckQIAAAQIECBAgQIAAgd4SOOmkk2LGjBkxd+7c3roxd0OAAIGaCfj1T80q1OMQIECAAAECBAgQIECAAAECBAgQINAUEIA2LawRIECAAAECBAgQIECAAAECBAgQIFAzAQFozSrU4xAgQIAAAQIECBAgQIAAAQIECBAg0BQQgDYtrBEgQIAAAQIECBAgQIAAAQIECBAgUDMBAWjNKtTjECBAgAABAgQIECBAgAABAgQIECDQFBCANi2sESBAgAABAgQIECBAgAABAgQIECBQMwEBaM0q1OMQIECAAAECBAgQIECAAAECBAgQINAUEIA2LawRIECAAAECBAgQIECAAAECBAgQIFAzAQFozSrU4xAgQIAAAQIECBAgQIAAAQIECBAg0BQQgDYtrBEgQIAAAQIECBAgQIAAAQIECBAgUDMBAWjNKtTjECBAgAABAgQIECBAgAABAgQIECDQFBCANi2sESBAgAABAgQIECBAgAABAgQIECBQMwEBaM0q1OMQIECAAAECBAgQIECAAAECBAgQINAUEIA2LawRIECAAAECBAgQIECAAAECBAgQIFAzAQFozSrU4xAgQIAAAQIECBAgQIAAAQIECBAg0BQQgDYtrBEgQIAAAQIECBAgQIAAAQIECBAgUDMBAWjNKtTjECBAgAABAgQIECBAgAABAgQIECDQFBCANi2sESBAgAABAgQIECBAgAABAgQIECBQMwEBaM0q1OMQIECAAAECBAgQIECAAAECBAgQINAUEIA2LawRIECAAAECBAgQIECAAAECBAgQIFAzAQFozSrU4xAgQIAAAQIECBAgQIAAAQIECBAg0BQQgDYtrBEgQIAAAQIECBAgQIAAAQIECBAgUDMBAWjNKtTjECBAgAABAgQIECBAgAABAgQIECDQFBCANi2sESBAgAABAgQIECBAgAABAgQIECBQMwEBaM0q1OMQIECAAAECBAgQIECAAAECBAgQINAUEIA2LawRIECAAAECBAgQIECAAAECBAgQIFAzAQFozSrU4xAgQIAAAQIECBAgQIAAAQIECBAg0BQQgDYtrBEgQIAAAQIECBAgQIAAAQIECBAgUDMBAWjNKtTjECBAgAABAgQIECBAgAABAgQIECDQFBCANi2sESBAgAABAgQIECBAgAABAgQIECBQMwEBaM0q1OMQIECAAAECBAgQIECAAAECBAgQINAUEIA2LawRIECAAAECBAgQIECAAAECBAgQIFAzAQFozSrU4xAgQIAAAQIECBAgQIAAAQIECBAg0BQQgDYtrBEgQIAAAQIECBAgQIAAAQIECBAgUDMBAWjNKtTjECBAgAABAgQIECBAgAABAgQIECDQFBCANi2sESBAgAABAgQIECBAgAABAgQIECBQMwEBaM0q1OMQIECAAAECBAgQIECAAAECBAgQINAUEIA2LawRIECAAAECBAgQIECAAAECBAgQIFAzAQFozSrU4xAgQIAAAQIECBAgQIAAAQIECBAg0BQQgDYtrBEgQIAAAQIECBAgQIAAAQIECBAgUDMBAWjNKtTjECBAgAABAgQIECBAgAABAgQIECDQFBCANi2sESBAgAABAgQIECBAgAABAgQIECBQMwEBaM0q1OMQIECAAAECBAgQIECAAAECBAgQINAUEIA2LawRIECAAAECBAgQIECAAAECBAgQIFAzAQFozSrU4xAgQIAAAQIECBAgQIAAAQIECBAg0BQQgDYtrBEgQIAAAQIECBAgQIAAAQIECBAgUDMBAWjNKtTjECBAgAABAgQIECBAgAABAgQIECDQFBCANi2sESBAgAABAgQIECBAgAABAgQIECBQMwEBaM0q1OMQIECAAAECBAgQIECAAAECBAgQINAUEIA2LawRIECAAAECBAgQIECAAAECBAgQIFAzAQFozSrU4xAgQIAAAQIECBAgQIAAAQIECBAg0BQQgDYtrBEgQIAAAQIECBAgQIAAAQIECBAgUDMBAWjNKtTjECBAgAABAgQIECBAgAABAgQIECDQFBCANi2sESBAgAABAgQIECBAgAABAgQIECBQMwEBaM0q1OMQIECAAAECBAgQIECAAAECBAgQINAUEIA2LawRIECAAAECBAgQIECAAAECBAgQIFAzAQFozSrU4xAgQIAAAQIECBAgQIAAAQIECBAg0BQQgDYtrBEgQIAAAQIECBAgQIAAAQIECBAgUDMBAWjNKtTjECBAgAABAgQIECBAgAABAgQIECDQFBCANi2sESBAgAABAgQIECBAgAABAgQIECBQMwEBaM0q1OMQIECAAAECBAgQIECAAAECBAgQINAUEIA2LawRIECAAAECBAgQIECAAAECBAgQIFAzAQFozSrU4xAgQIAAAQIECBAgQIAAAQIECBAg0BQQgDYtrBEgQIAAAQIECBAgQIAAAQIECBAgUDMBAWjNKtTjECBAgAABAgQIECBAgAABAgQIECDQFBCANi2sESBAgAABAgQIECBAgAABAgQIECBQMwEBaM0q1OMQIECAAAECBAgQIECAAAECBAgQINAUEIA2LawRIECAAAECBAgQIECAAAECBAgQIFAzAQFozSrU4xAgQIAAAQIECBAgQIAAAQIECBAg0BQQgDYtrBEgQIAAAQIECBAgQIAAAQIECBAgUDMBAWjNKtTjECBAgAABAgQIECBAgAABAgQIECDQFJjWXK3H2vLly+Oiiy6K6667Lp555pnYZ5994pBDDok3velN0d/fv84Pedlll8XPfvazeOihh2JwcDBe9KIXxate9ap4wxve0PFa+fibb76543t553bbbRcnnnjiuO97gwABAgQIECBAgAABAgQIECBAgACB7gnUKgBdtGhRfOADH4gHH3ywEpo7d258//vfr5arrroqTjvttJg+ffpa6eUg9c///M/jxhtvrI7faqutqtc777wzfvSjH8V3v/vd+NznPhezZs1qud7FF18c119/fcu+0Rt77rmnAHQ0iHUCBAgQIECAAAECBAgQIECAAAECG1CgVgHo6aefXoWfRxxxRHziE5+IrbfeOh5++OH42Mc+FldccUWcc845ccopp6wV59///d9X4ecee+wRH//4x2O//farzluwYEF8+tOfjptuuim++MUvxqmnntpyvbvuuqva/tCHPhQzZsxoeS9vbLnllmP22UGAAAECBAgQIECAAAECBAgQIECAwIYRqE0Aevvtt8e1115btcg844wzYubMmZXYrrvuGp///OfjbW97W1x66aXx/ve/f40h5NKlS6sWnn19fVXYOX/+/BH9/fffP84888z4gz/4g7jkkkvigx/8YGy++ebV+wsXLowlS5bEtttuGyeccMLIOVYIECBAgAABAgQIECBAgAABAgQIENg0ArWZBOnyyy+vBI855piR8LNBmrvCH3744bFixYoqBG3sH+/1lltuiYGBgZg3b16MDj8bx+d922+/fZRlGffcc09jdzRafzZai468YYUAAQIECBAgQIAAAQIECBAgQIAAgU0iUJsA9LbbbqsAc/f3TiUHoLmsboKixnn52DzG59/8zd80drW8rlq1KhYvXlztmzNnzsh77QFoPu7pp58eed8KAQIECBAgQIAAAQIECBAgQIAAAQIbV6A2XeDzWJ+5jA4kR1M29jcmSBr9Xvt6URSxzTbbtO8e2f7hD39YtSbNY4zmLvaN0ghAc/B58sknV2OI5pakedzPV7ziFZHHBc3d4xUCBAgQIECAAAECBAgQIECAAAECBDaOQG0C0Oeff74SawSd7XyNWdwbx7W/v7bbjzzySJx77rnV4e973/sih6WNcvfdd1erX/va16K/vz/yeKE5DM3d5H/yk5/EddddF2effXbsvffejVPGvH7ve9+rZpcf80ba0XiG3Pr08ccf73SIfQQIECCQBPIQJY3y3HPPxfr+7G9cyysBAgQIECBAoBsCo/+tsnz5ct/vuoHqGgQI1FYg/5zMZXBwcMLPWIsANAO88MILFcJ4s6zPnj27er+BNhGxp556Kj784Q/HokWLqjFF3/rWt45cJn+5zuFoLscff3x85CMfqSZkytt5/6c+9anIEzWdddZZcd5558W0aZ3p83Uee+yxfNqY0rj3/Jfl+lT6mAvbQYAAgRoL5J+Zo79k1PhRPRoBAgQIECAwSQV8v5ukFee2CRDYKALd+BnZOYXbKLffvQ/Js7XPmjUrli1bFo2QsP3qjf3Tp09vf2utth944IE45ZRT4tFHH40DDzywmh1+9In5888///x48skn45BDDmlpGbrLLrtUx5944onVREl5tvojjzxy9Okj6zkYnTFjxsj26JXx9o8+xjoBAgQIECBAgAABAgQIECBAgAABAk2BWgSg+XG22267yON7Pvvss82nG7XW2L/FFluM2rt2q3nipI9+9KPVtQ877LA444wzov06OYTNs8bnpVPZcccd4+CDD666wecu8eMFoG9729siL51KboGaz8vd/HfeeedOh9hHgAABAklg6dKlI5PV5Z4BjV4AcAgQIECAAAECvSCwcuXKqvFMvpfc0GXu3Lm9cFvugQABAj0p0GjUmLO3iZaJnznRT9xA5+UANJdG0Nn+MUuWLKl2rW5yo/Zz8nYeu/PP/uzPquvmru15Zvj28LPTeZ327bDDDtXuHGQqBAgQIECAAAECBAgQIECAAAECBAhseIHatABthIu5deUrX/nKMXJ5fy4HHHDAmPfG2/Hv//7vIxMSvfvd7473vOc94x0ad955Z1xzzTWx+eabxwknnNDxuIULF1b7d9ttt47v20mAAAECBAgQIECAAAECBAgQIECAQHcFatMC9LjjjqtkfvzjH48RyoOl5pacueTxOdem5DAzt/bMs7zn7u+rCz/z9Z555pn40pe+FF/84hfj/vvvH/MRTz/9dNx2223V/oMOOmjM+3YQIECAAAECBAgQIECAAAECBAgQINB9gdoEoLnV5x577FFNMnTppZe2SH3961+P3O189913jyOOOKLlvSuvvDJ+9KMfxb333juyP48t8IUvfKGaNfi9731vvOUtbxl5b7yVHKzmsTnzTMNf+cpXYtWqVSOH5hnqP/vZz1aTNL361a+O/ffff+Q9KwQIECBAgAABAgQIECBAgAABAgQIbDiB2nSBzy01TzrppPjkJz8ZZ511Vlx99dWxzz77xC233FKtb7bZZnHqqae2zM6eWc8+++xqZvd87vz58yvpiy66KB555JFq/ctf/nLkZbySJ0Q66qijYubMmXHaaafFhz/84citUK+//vp44xvfGP39/XHFFVfEQw89VF3/5JNPHu9S9hMgQIAAAQIECBAgQIAAAQIECBAg0GWB2gSg2eXoo4+uWm7mAPSyyy6rlrw/twzNweNLXvKSvLnG8stf/nLkmIGBgZH1Tiu5e32j5Bnizz333CpUveOOO+Kb3/xm9dasWbMiT6CUw9E8RqhCgAABAgQIECBAgAABAgQIECBAgMDGEahVAJrJXvayl8WFF15YdXl/8MEHI0+OtNNOO0VfX+fe/hdccMEY6c997nNj9q3tjjy+53nnnReLFy+O/PlbbrllzJs3b9zPX9vrOo4AAQIECBAgQIAAAQIECBAgQIAAgXUXqF0A2iDYdtttIy+bqmy99daRF4UAAQIECBAgQIAAAQIECBAgQIAAgU0n0LlZ5Ka7H59MgAABAgQIECBAgAABAgQIECBAgACBrgkIQLtG6UIECBAgQIAAAQIECBAgQIAAAQIECPSagAC012rE/RAgQIAAAQIECBAgQIAAAQIECBAg0DUBAWjXKF2IAAECBAgQIECAAAECBAgQIECAAIFeExCA9lqNuB8CBAgQIECAAAECBAgQIECAAAECBLomIADtGqULESBAgAABAgQIECBAgAABAgQIECDQawIC0F6rEfdDgAABAgQIECBAgAABAgQIECBAgEDXBASgXaN0IQIECBAgQIAAAQIECBAgQIAAAQIEek1AANprNeJ+CBAgQIAAAQIECBAgQIAAAQIECBDomoAAtGuULkSAAAECBAgQIECAAAECBAgQIECAQK8JCEB7rUbcDwECBAgQIECAAAECBAgQIECAAAECXRMQgHaN0oUIECBAgAABAgQIECBAgAABAgQIEOg1AQFor9WI+yFAgAABAgQIECBAgAABAgQIECBAoGsCAtCuUboQAQIECBAgQIAAAQIECBAgQIAAAQK9JiAA7bUacT8ECBAgQIAAAQIECBAgQIAAAQIECHRNQADaNUoXIkCAAAECBAgQIECAAAECBAgQIECg1wQEoL1WI+6HAAECBAgQIECAAAECBAgQIECAAIGuCQhAu0bpQgQIECBAgAABAgQIECBAgAABAgQI9JqAALTXasT9ECBAgAABAgQIECBAgAABAgQIECDQNQEBaNcoXYgAAQIECBAgQIAAAQIECBAgQIAAgV4TEID2Wo24HwIECBAgQIAAAQIECBAgQIAAAQIEuiYgAO0apQsRIECAAAECBAgQIECAAAECBAgQINBrAgLQXqsR90OAAAECBAgQIECAAAECBAgQIECAQNcEBKBdo3QhAgQIECBAgAABAgQIECBAgAABAgR6TUAA2ms14n4IECBAgAABAgQIECBAgAABAgQIEOiagAC0a5QuRIAAAQIECBAgQIAAAQIECBAgQIBArwkIQHutRtwPAQIECBAgQIAAAQIECBAgQIAAAQJdExCAdo3ShQgQIECAAAECBAgQIECAAAECBAgQ6DUBAWiv1Yj7IUCAAAECBAgQIECAAAECBAgQIECgawIC0K5RuhABAgQIECBAgAABAgQIECBAgAABAr0mIADttRpxPwQIECBAgAABAgQIECBAgAABAgQIdE1AANo1ShciQIAAAQIECBAgQIAAAQIECBAgQKDXBASgvVYj7ocAAQIECBAgQIAAAQIECBAgQIAAga4JCEC7RulCBAgQIECAAAECBAgQIECAAAECBAj0moAAtNdqxP0QIECAAAECBAgQIECAAAECBAgQINA1AQFo1yhdiAABAgQIECBAgAABAgQIECBAgACBXhMQgPZajbgfAgQIECBAgAABAgQIECBAgAABAgS6JiAA7RqlCxEgQIAAAQIECBAgQIAAAQIECBAg0GsCAtBeqxH3Q4AAAQIECBAgQIAAAQIECBAgQIBA1wQEoF2jdCECBAgQIECAAAECBAgQIECAAAECBHpNQADaazXifggQIECAAAECBAgQIECAAAECBAgQ6JqAALRrlC5EgAABAgQIECBAgAABAgQIECBAgECvCQhAe61G3A8BAgQIECBAgAABAgQIECBAgAABAl0TEIB2jdKFCBAgQIAAAQIECBAgQIAAAQIECBDoNQEBaK/ViPshQIAAAQIECBAgQIAAAQIECBAgQKBrAgLQrlG6EAECBAgQIECAAAECBAgQIECAAAECvSYgAO21GnE/BAgQIECAAAECBAgQIECAAAECBAh0TUAA2jVKFyJAgAABAgQIECBAgAABAgQIECBAoNcEBKC9ViPuhwABAgQIECBAgAABAgQIECBAgACBrgkIQLtG6UIECBAgQIAAAQIECBAgQIAAAQIECPSagAC012rE/RAgQIAAAQIECBAgQIAAAQIECBAg0DUBAWjXKF2IAAECBAgQIECAAAECBAgQIECAAIFeExCA9lqNuB8CBAgQIECAAAECBAgQIECAAAECBLomIADtGqULESBAgAABAgQIECBAgAABAgQIECDQawIC0F6rEfdDgAABAgQIECBAgAABAgQIECBAgEDXBASgXaN0IQIECBAgQIAAAQIECBAgQIAAAQIEek1AANprNeJ+CBAgQIAAAQIECBAgQIAAAQIECBDomoAAtGuULkSAAAECBAgQIECAAAECBAgQIECAQK8JCEB7rUbcDwECBAgQIECAAAECBAgQIECAAAECXRMQgHaN0oUIECBAgAABAgQIECBAgAABAgQIEOg1AQFor9WI+yFAgAABAgQIECBAgAABAgQIECBAoGsCAtCuUboQAQIECBAgQIAAAQIECBAgQIAAAQK9JiAA7bUacT8ECBAgQIAAAQIECBAgQIAAAQIECHRNQADaNUoXIkCAAAECBAgQIECAAAECBAgQIECg1wQEoL1WI+6HAAECBAgQIECAAAECBAgQIECAAIGuCQhAu0bpQgQIECBAgAABAgQIECBAgAABAgQI9JqAALTXasT9ECBAgAABAgQIECBAgAABAgQIECDQNQEBaNcoXYgAAQIECBAgQIAAAQIECBAgQIAAgV4TEID2Wo24HwIECBAgQIAAAQIECBAgQIAAAQIEuiYgAO0apQsRIECAAAECBAgQIECAAAECBAgQINBrAgLQXqsR90OAAAECBAgQIECAAAECBAgQIECAQNcEBKBdo3QhAgQIECBAgAABAgQIECBAgAABAgR6TUAA2ms14n4IECBAgAABAgQIECBAgAABAgQIEOiagAC0a5QuRIAAAQIECBAgQIAAAQIECBAgQIBArwkIQHutRtwPAQIECBAgQIAAAQIECBAgQIAAAQJdExCAdo3ShQgQIECAAAECBAgQIECAAAECBAgQ6DUBAWiv1Yj7IUCAAAECBAgQIECAAAECBAgQIECgawIC0K5RuhABAgQIECBAgAABAgQIECBAgAABAr0mMK3Xbsj9rFlg2bJlsWTJkjUf6AgCBAhMUYGVK1eOPPkLL7wQg4ODI9tWCBAgQIAAAQKbWmD0v01WrVrl+92mrhCfT4BATwssX768ur+yLCd8nwLQCdNtuhNXrFgROQRVCBAgQKCzwOi/GPOXioGBgc4H2kuAAAECBAgQ2AQCo/+tkv+d4vvdJqgEH0mAwKQREIBOmqrq7o1uvfXWseOOO3b3oq5GgACBGgksXbo0Fi9eXD3R7NmzIy8KAQIECBAgQKBXBHJvlSeffLK6nRkzZsTcuXN75dbcBwECBHpOoBGA9vVNfCTPiZ/ZcxxuiAABAgQIECBAgAABAgQIECBAgAABAq0CAtBWD1sECBAgQIAAAQIECBAgQIAAAQIECNRIQABao8r0KAQIECBAgAABAgQIECBAgAABAgQItAoIQFs9bBEgQIAAAQIECBAgQIAAAQIECBAgUCMBAWiNKtOjECBAgAABAgQIECBAgAABAgQIECDQKiAAbfWwRYAAAQIECBAgQIAAAQIECBAgQIBAjQQEoDWqTI9CgAABAgQIECBAgAABAgQIECBAgECrgAC01cMWAQIECBAgQIAAAQIECBAgQIAAAQI1EhCA1qgyPQoBAgQIECBAgAABAgQIECBAgAABAq0CAtBWD1sECBAgQIAAAQIECBAgQIAAAQIECNRIQABao8r0KAQIECBAgAABAgQIECBAgAABAgQItAoIQFs9bBEgQIAAAQIECBAgQIAAAQIECBAgUCMBAWiNKtOjECBAgAABAgQIECBAgAABAgQIECDQKiAAbfWwRYAAAQIECBAgQIAAAQIECBAgQIBAjQQEoDWqTI9CgAABAgQIECBAgAABAgQIECBAgECrgAC01cMWAQIECBAgQIAAAQIECBAgQIAAAQI1EhCA1qgyPQoBAgQIECBAgAABAgQIECBAgAABAq0CAtBWD1sECBAgQIAAAQIECBAgQIAAAQIECNRIQABao8r0KAQIECBAgAABAgQIECBAgAABAgQItAoIQFs9bBEgQIAAAQIECBAgQIAAAQIECBAgUCMBAWiNKtOjECBAgAABAgQIECBAgAABAgQIECDQKiAAbfWwRYAAAQIECBAgQIAAAQIECBAgQIBAjQQEoDWqTI9CgAABAgQIECBAgAABAgQIECBAgECrgAC01cMWAQIECBAgQIAAAQIECBAgQIAAAQI1EhCA1qgyPQoBAgQIECBAgAABAgQIECBAgAABAq0CAtBWD1sECBAgQIAAAQIECBAgQIAAAQIECNRIQABao8r0KAQIECBAgAABAgQIECBAgAABAgQItAoIQFs9bBEgQIAAAQIECBAgQIAAAQIECBAgUCMBAWiNKtOjECBAgAABAgQIECBAgAABAgQIECDQKiAAbfWwRYAAAQIECBAgQIAAAQIECBAgQIBAjQQEoDWqTI9CgAABAgQIECBAgAABAgQIECBAgECrgAC01cMWAQIECBAgQIAAAQIECBAgQIAAAQI1EhCA1qgyPQoBAgQIECBAgAABAgQIECBAgAABAq0CAtBWD1sECBAgQIAAAQIECBAgQIAAAQIECNRIQABao8r0KAQIECBAgAABAgQIECBAgAABAgQItAoIQFs9bBEgQIAAAQIECBAgQIAAAQIECBAgUCMBAWiNKtOjECBAgAABAgQIECBAgAABAgQIECDQKiAAbfWwRYAAAQIECBAgQIAAAQIECBAgQIBAjQQEoDWqTI9CgAABAgQIECBAgAABAgQIECBAgECrgAC01cMWAQIECBAgQIAAAQIECBAgQIAAAQI1EhCA1qgyPQoBAgQIECBAgAABAgQIECBAgAABAq0CAtBWD1sECBAgQIAAAQIECBAgQIAAAQIECNRIQABao8r0KAQIECBAgAABAgQIECBAgAABAgQItAoIQFs9bBEgQIAAAQIECBAgQIAAAQIECBAgUCMBAWiNKtOjECBAgAABAgQIECBAgAABAgQIECDQKiAAbfWwRYAAAQIECBAgQIAAAQIECBAgQIBAjQQEoDWqTI9CgAABAgQIECBAgAABAgQIECBAgECrgAC01cMWAQIECBAgQIAAAQIECBAgQIAAAQI1EhCA1qgyPQoBAgQIECBAgAABAgQIECBAgAABAq0CAtBWD1sECBAgQIAAAQIECBAgQIAAAQIECNRIQABao8r0KAQIECBAgAABAgQIECBAgAABAgQItAoIQFs9bBEgQIAAAQIECBAgQIAAAQIECBAgUCMBAWiNKtOjECBAgAABAgQIECBAgAABAgQIECDQKiAAbfWwRYAAAQIECBAgQIAAAQIECBAgQIBAjQQEoDWqTI9CgAABAgQIECBAgAABAgQIECBAgECrgAC01cMWAQIECBAgQIAAAQIECBAgQIAAAQI1EhCA1qgyPQoBAgQIECBAgAABAgQIECBAgAABAq0CAtBWD1sECBAgQIAAAQIECBAgQIAAAQIECNRIQABao8r0KAQIECBAgAABAgQIECBAgAABAgQItAoIQFs9bBEgQIAAAQIECBAgQIAAAQIECBAgUCMBAWiNKtOjECBAgAABAgQIECBAgAABAgQIECDQKiAAbfWwRYAAAQIECBAgQIAAAQIECBAgQIBAjQQEoDWqTI9CgAABAgQIECBAgAABAgQIECBAgECrgAC01cMWAQIECBAgQIAAAQIECBAgQIAAAQI1EhCA1qgyPQoBAgQIECBAgAABAgQIECBAgAABAq0CAtBWD1sECBAgQIAAAQIECBAgQIAAAQIECNRIQABao8r0KAQIECBAgAABAgQIECBAgAABAgQItAoIQFs9bBEgQIAAAQIECBAgQIAAAQIECBAgUCMBAWiNKtOjECBAgAABAgQIECBAgAABAgQIECDQKiAAbfWwRYAAAQIECBAgQIAAAQIECBAgQIBAjQQEoDWqTI9CgAABAgQIECBAgAABAgQIECBAgECrgAC01cMWAQIECBAgQIAAAQIECBAgQIAAAQI1EhCA1qgyPQoBAgQIECBAgAABAgQIECBAgAABAq0CAtBWD1sECBAgQIAAAQIECBAgQIAAAQIECNRIQABao8r0KAQIECBAgAABAgQIECBAgAABAgQItAoIQFs9bBEgQIAAAQIECBAgQIAAAQIECBAgUCMBAWiNKtOjECBAgAABAgQIECBAgAABAgQIECDQKiAAbfWwRYAAAQIECBAgQIAAAQIECBAgQIBAjQQEoDWqTI9CgAABAgQIECBAgAABAgQIECBAgECrgAC01cMWAQIECBAgQIAAAQIECBAgQIAAAQI1EphWo2epHmX58uVx0UUXxXXXXRfPPPNM7LPPPnHIIYfEm970pujv71/nx12wYEFceOGFcf/998cWW2wRBx98cBx77LGx5557jnutiZwz7sW8QYAAAQIECBAgQIAAAQIECBAgQIDAhAVqFYAuWrQoPvCBD8SDDz5YgcydOze+//3vV8tVV10Vp512WkyfPn2tsXKQevbZZ1fHz549O1asWBE33HBDXHDBBfGZz3wmDj300DHXmsg5Yy5iBwECBAgQIECAAAECBAgQIECAAAECXRGoVRf4008/vQo/jzjiiLjkkkvi4osvjn/7t3+LvfbaK6644oo455xz1hrtlltuqY7PgemZZ54Z//Ef/1EFqR/60Idi2bJlccopp8Rjjz3Wcr2JnNNyARsECBAgQIAAAQIECBAgQIAAAQIECHRVoDYB6O233x7XXnttzJo1K84444zYeuutK6hdd901Pv/5z1fd3y+99NJ49tln1wrwq1/9apRlGe9617vi6KOPjqIoYrPNNosTTjgh3v72t8fKlSvjO9/5Tsu1JnJOywVsECBAgAABAgQIECBAgAABAgQIECDQVYHaBKCXX355BXPMMcfEzJkzW5ByV/jDDz+86sKeQ9A1laVLl1Zhaj7u+OOPH3N4Y19uZbpq1arq/YmcM+bCdhAgQIAAAQIECBAgQIAAAQIECBAg0FWB2gSgt912WwWTu793KjkAzeXmm2/u9HbLvjvuuKNq/Tlv3rzYZZddWt7LG/vvv39sueWWsXjx4njggQeq9ydyzpgL20GAAAECBAgQIECAAAECBAgQIECAQFcFajMJ0sMPP1zBzJkzpyNQY39jgqSOBw3vXNO18mH5erk7fb5enhF+Iud0uoc8e30eu7RT+f/buxd4q6f8/+Of0/1+VVRSVCrNJHJJUURuzRQpE02aqEREg6TwGD3cL0MlQiNSk6iMBlFRKOUWuUyju0SYCt1Ll7/3+v3XfnzPPvt0ztnntvf3+1qPx2nv872u9Vwezvp+vutSqtT/VZd6myr4SkIAAQQQSCzge+dr765du2zfvn2JD2QrAggggAACCCBQDAL79++P3VXtFp7vYhx8QQABBLII7N69220L/r8zy0E5bAhNAHT79u2uqD7QGV/uKlWquE3+uPj9wd/9MdldS8fGXy+Zc4L39N9Xr17tVpn3vwc/q1ev7n7VavQKgpIQQAABBHIW0JzN+iEhgAACCCCAAAKpKKAXtTzfpWLNkCcEEEgVAR8AzU9+QjEEXhFg9fBR0tD0RKlSpUpuc27Q/B+f7K6lC/nr+fsmc06ifLINAQQQQAABBBBAAAEEEEAAAQQQQAABBApOIBQ9QEuUKOFWf9+5c6dlF+D028uUKZOjXsWKFd0x6mmZXfLXK1u2rDskmXMSXfuMM86wSZMmJdrl3goOGDDABV+1sBMJAQQQQCCxgP4f7Xvmly9f3v2NSHwkWxFAAAEEEEAAgaIXUK9PP+y9dOnS2XbkKfqccUcEEEAg9QR8DC4jIyPpzIUiAKrSH3LIIW4+Ts3LmSj57T5QmegYv03XUtqyZYvflOUz/nrJnJPlor9tqFWrlvtJtG/Tpk1us+YC9YHXRMexDQEEEIi6QHDOT/6fGfX/Gig/AggggAACqScQnJ5HHXp4vku9OiJHCCCQegL5CYCGYgi8qsQHIH1gMr6afDDTz6MZvz/4e07X0rHx10vmnOA9+Y4AAggggAACCCCAAAIIIIAAAggggAACBS8QmgBo7dq1nY4WEUqU/PbmzZsn2p1pm7+WVngPvpnzB2mowubNm01v6po0aeI2J3OOvx6fCCCAAAIIIIAAAggggAACCCCAAAIIIFA4AqEJgJ555plOaO7cuVmktEjSW2+95ba3atUqy/74DXXr1rVmzZrZtm3b7P3334/fbfPmzTMNr9QxFSpUcPuTOSfLhdmAAAIIIIAAAggggAACCCCAAAIIIIAAAgUqEJoAaJs2baxhw4a2YsUKmzVrViakyZMnm+bPbNCggZ188smZ9i1cuNDmzJlja9asybT9kksucb9PmDDBgsPqf/zxR5syZYrb16NHj3yfk+kC/IIAAggggAACCCCAAAIIIIAAAggggAACBSoQmkWQNBFq//797fbbb7e7777bFi1a5Ianf/755+67VtYbOnSoxU+YOmrUKNuwYYM798gjj4zhdujQwTRcftmyZdavXz/T6ux79+419TBVMLVdu3bWsWPH2PH6ksw5mS7ALwgggAACCCCAAAIIIIAAAggggAACCCBQoAKhCYBKpX379vbwww+7AKiGqetHST1DhwwZYi1btnS/5+afkiVL2pgxY9z1Zs+ebepFqqTt3bt3tyuvvNLNARq8VjLnBM/nOwIIIIAAAggggAACCCCAAAIIIIAAAggUrEDGgd9SwV4yNa6mXppaxEiLEx122GFZgpV5yaV6fq5atcpEVb9+fatYsWKOpydzTk4XVZnatm1rjzzyiJ133nk5Hc5+BBBAILICO3bsMC1Yp1S5cmWrVKlSZC0oOAIIIIAAAgiknoAW2924caPLWNmyZa1GjRqpl0lyhAACCKSIwO7du12nxrvuust1SkwmW6HqARoEqFmzpumnIFKpUqWsadOmebpUMufk6QYcjAACCCCAAAIIIIAAAggggAACCCCAAAI5CoRmEaQcS8oBCCCAAAIIIIAAAggggAACCCCAAAIIIBA5AQKgkatyCowAAggggAACCCCAAAIIIIAAAggggEB0BAiARqeuKSkCCCCAAAIIIIAAAggggAACCCCAAAKREyAAGrkqp8AIIIAAAggggAACCCCAAAIInRpOcQAAKVVJREFUIIAAAghER4AAaHTqmpIigAACCCCAAAIIIIAAAggggAACCCAQOQECoJGrcgqMAAIIIIAAAggggAACCCCAAAIIIIBAdAQIgEanrikpAggggAACCCCAAAIIIIAAAggggAACkRMgABq5KqfACCCAAAIIIIAAAggggAACCCCAAAIIREeAAGh06pqSIoAAAggggAACCCCAAAIIIIAAAgggEDkBAqCRq3IKjAACCCCAAAIIIIAAAggggAACCCCAQHQECIBGp64pKQIIIIAAAggggAACCCCAAAIIIIAAApETIAAauSqnwAgggAACCCCAAAIIIIAAAggggAACCERHgABodOqakiKAAAIIIIAAAggggAACCCCAAAIIIBA5AQKgkatyCowAAggggAACCCCAAAIIIIAAAggggEB0BAiARqeuKSkCCCCAAAIIIIAAAggggAACCCCAAAKREyAAGrkqp8AIIIAAAggggAACCCCAAAIIIIAAAghER4AAaHTqmpIigAACCCCAAAIIIIAAAggggAACCCAQOQECoJGrcgqMAAIIIIAAAggggAACCCCAAAIIIIBAdAQIgEanrikpAggggAACCCCAAAIIIIAAAggggAACkRMgABq5KqfACCCAAAIIIIAAAggggAACCCCAAAIIREeAAGh06pqSIoAAAggggAACCCCAAAIIIIAAAgggEDkBAqCRq3IKjAACCCCAAAIIIIAAAggggAACCCCAQHQECIBGp64pKQIIIIAAAggggAACCCCAAAIIIIAAApETKBW5EoegwI8++qhNmTIlBCWhCAgggEDhCOzbt8/27t3rLl6qVCkrWbJk4dyIqyKAAAIIIIAAAkkIHDhwwPbs2ePOLFGihJUuXTqJq3AKAgggEA2B/fv357ugBEDzTVh0Fyhbtqx16tSp6G7InRBAAIE0Fdi5c6f9/PPPLvfly5e3SpUqpWlJyDYCCCCAAAIIhFFAL2p37NjhilauXDmrUqVKGItJmRBAAIECE1A8rF69eklfL+O3N08Hkj6bExFAAAEEEEhBgenTp9vw4cNdzm688Ubr379/CuaSLCGAAAIIIIBAVAVWrlxpnTt3dsU/66yzbOzYsVGloNwIIIBAkQgwB2iRMHMTBBBAAAEEEEAAAQQQQAABBBBAAAEEECgOAQKgxaHOPRFAAAEEEEAAAQQQQAABBBBAAAEEEECgSAQIgBYJMzdBAAEEEEAAAQQQQAABBBBAAAEEEEAAgeIQIABaHOrcEwEEEEAAAQQQQAABBBBAAAEEEEAAAQSKRIAAaJEwcxMEEEAAAQQQQAABBBBAAAEEEEAAAQQQKA6BUsVxU+6JAAIIIIBAYQqUKFHC9KPkPwvzflwbAQQQQAABBBDIi0BGRkasjVKyZMm8nMqxCCCAAAJJCGQc+C0lcR6nIIAAAggggAACCCCAAAIIIIAAAggggAACKS/AEPiUryIyiAACCCCAAAIIIIAAAggggAACCCCAAALJChAATVaO8xBAAAEEEEAAAQQQQAABBBBAAAEEEEAg5QUIgKZ8FZFBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEhWgABosnKchwACCCCAAAIIIIAAAggggAACCCCAAAIpL0AANOWriAwigAAC0RHYv3+/7dmzJzoFpqQIIIAAAgggkHYCu3fvTrs8k2EEEEAg6gKlog5A+RFAAAEEildg3bp1NnXqVFuxYoWtXr3a9u7da/Xr17fGjRtb27ZtrVOnTgWWwX79+rlrjR492ipUqOC+X3vttbZz50677777rGbNmrm615gxY2zp0qWm67Vp0yZX53AQAggggAACCKSnwIEDB2zGjBn24Ycf2sqVK+2HH36wqlWrWqNGjaxJkybWs2dPO+SQQwqkcInaKnPmzHFtpXbt2lnfvn1zdR/aKrli4iAEEIiQAAHQCFU2RUUAAQRSTWDhwoU2cuRI27Fjh8ta+fLlrXLlyrZ27Vr3M3fuXPv000/t+uuvt9KlS+c7+1999ZW7xr59+2LXWr58ubu/Aq+5TevXrzdda+vWrbk9heMQQAABBBBAIA0Ftm/fbnfeeactWLDA5b5EiRJWvXp10/YlS5a4H7VX7rrrLmvRokW+S5iorbJ582bX7lDANbeJtkpupTgOAQSiIkAANCo1TTkRQACBFBNYtWqVDRs2zOWqV69edvHFF1uNGjXc7xpa9uabb9pjjz1mM2fOtF9//dWGDx9eKCXo2rWrG3bve4QWyk24KAIIIIAAAgikpcC9997rgp8NGza0wYMH2/HHH28lS5Z0Zfn6669t/PjxNn/+fLvmmmvs2WeftSOOOKLAy3n00UfbRRddZMccc0yBX5sLIoAAAlERYA7QqNQ05UQAAQRSTMD3pPjDH/5gAwcOjAU/lc2yZcva+eefb3/7299crvVgUVhzg1599dWuh6l6npIQQAABBBBAAAEvoBeyixcvtoyMDHvwwQftxBNPjAU/dUyDBg3sjjvusFatWrkpfN5++21/aoF+Hnfcca6tcvbZZxfodbkYAgggECUBAqBRqm3KigACCKSQgIa2Kx111FHZ5uqEE06wOnXquDk6P/vss4THaei6hou9/vrrpjmy1qxZ4x5CEh6cYKN6bxzsHA3Pf+edd9zPzz//nOAKbEIAAQQQQACBMApompxdu3a56Xmym+NTQ+I7d+7siv/+++8nZMhvW+WXX35xbZWNGzcmvD5tlYQsbEQAAQQyCTAEPhMHvyCAAAIIFJVA06ZN7aOPPrJXXnnFunXrlqlHRTAPzzzzjJUrV870gBGf1CtDCxp98803mXZp+NmIESNyNVRswIABbg7QadOm2aGHHhq7jh4m1ANVCx4E5wdVb1USAggggAACCIRfQC9pNdx9y5Yt7kXoGWeckbDQZ511lp1++umuvRJ/QEG0VfSS99FHH3WjY2655ZbYLWirxCj4ggACCOQoQAA0RyIOQAABBBAoDIH27dvb888/71Z+v+yyy+zCCy+0k046KcvcWdnNzfnJJ5/YTTfd5LLWsWNHt2K8VmlVb813333XrrrqKnvooYdMvUjzmnQdLXiwaNEit8Kr8qZ86Lrjxo0rkAWZ8ponjkcAAQQQQACBohWoWLGitWnTxrRoo4a6K5ipIKiGvOvlrE+lSpUy/cQn2irxIvyOAAIIFJ9A1v9LF19euDMCCCCAQIQENJH//fffb7feequtW7fORo0a5UqvXpia66p169bWrl07N+wsnkU9Hu6++263edCgQdazZ8/YIeeee649/fTTNmHCBBcA1YIEZcqUie3PzZepU6e6YKcWHRg7dmzsIadTp072xBNP2KRJk3JzGY5BAAEEEEAAgTQXUOBTP3oJ+tprr7mf0qVLuxXf1V5RgDTR4kS0VdK84sk+AgiETiDreMLQFZECIYAAAgikqoB6fCpY2b17d6tdu7bL5g8//ODm87zrrrusS5cubr96ZAaT5tj6/vvvXW/RHj16BHe57+pRqrlD169f73ptZDkghw3q6aHUp0+fWPDTn9K7d++EQVm/n08EEEAAAQQQCI+AFmbUqBBNraMV4NXT89dffzXNZa6XrVdeeaVrL2hO8WCirRLU4DsCCCBQ/AIEQIu/DsgBAgggEGmBww8/3K677jqbPn2661k5ZMgQ69Chg5UvX97NvamHi2HDhrl5Oj3U2rVr3VcNb9fcXPFJDyfqlaEU/0ASf2yi31euXOk2t2zZMstuDYX//e9/n2U7GxBAAAEEEEAgnAKah1wjTDRaRb1ANYJFo08aNWrkCrx69Wrr37+/LViwIAZAWyVGwRcEEEAgJQQYAp8S1UAmEEAAAQQk0KBBA/ejRZF2795tEydOtMmTJ9t7771nb7zxhpsnVMf5oKZ6eWaX6tat63apF2heknqgbtu2zQ2br1atWsJTa9WqlXA7GxFAAAEEEEAg3AJ6QXvKKae4H5V02bJldu+997o5zRUY1fQ9GRkZtFXC/Z8BpUMAgTQUoAdoGlYaWUYAAQTSXUA9JRTcnDFjRrZF0ZAz9abw83tqQSKf/EIDwdXZ/T7/qQCqUqIeov6YRJ9+vtB9+/bZ/v37Ex2ScKGDhAeyEQEEEEAAAQTSVmDmzJn25JNPuil1sitE8+bN3Xzhmhf0p59+sq+++sodSlslOzG2I4AAAsUjQAC0eNy5KwIIIBBpgQ0bNthTTz3lHhi0SMDBkh9urnN80rB5peA2v89/ao5QperVq/tNufrU8ZqPVAHQjRs3JjzHXzvhTjYigAACCCCAQCgEXn75ZXvuueds1qxZBy1PpUqV7KijjnLH+LYJbZWDkrETAQQQKHIBAqBFTs4NEUAAAQRatGjhhpjv2bPHzf15MJEPPvjA7T755JNjh/mHDC1W5Ht6xnb+9kVD2BcvXuw2tWrVKrgrV9+bNWvmjps/f36W4xWw/fzzz7NsZwMCCCCAAAIIhEtAix4pzZ49O9uXotqvnp8rVqxwo040P7kSbRXHwD8IIIBAyggQAE2ZqiAjCCCAQHQENLemX71dQ8vuuece09ybwbRr1y7XQ9QPk9fCSD6deuqp1qRJE9u0aZM7Rr01fdKw+NGjR9vWrVutYcOG5h9E/P7cfHbu3NnN36VeH36+UZ2n1ejHjRtnW7Zsyc1lOAYBBBBAAAEE0ljg4osvtipVqphGflxxxRVuTvL4F69ffvmlXX311W7aHAVMK1eu7EpMWyWNK56sI4BAKAVYBCmU1UqhEEAAgdQXGDhwoHtIeOKJJ9yKqlpVtWrVqi5oqaHn3333nQs4ak7OESNGZFp5Xaux3nDDDaYV41966SXTw4d6iGrOTvX8XLVqlR1xxBFutVY/B1deRNq2bWvK3+OPP24DBgywTp06Wc2aNU29Ub/44gurV6+effvtt3m5JMcigAACCCCAQJoJaNFDTdlz8803m1Z116faIFq0sUKFCm7b9u3bXan0wvWOO+6IlZC2SoyCLwgggEBKCBAATYlqIBMIIIBANAV69erlAp7Tpk2zNWvWuB6dS5cudRgKhjZt2tQthOSHpAeVNIxeCyk98MAD9vHHH9vy5cvdbj2snHPOOS6AWaNGjeApefp+6aWXmoboT5kyxTQHmJJ6dSgYq2FuL7zwQp6ux8EIIIAAAgggkH4CdevWNb2s1UtRTYGzbt0612ZRSRTk1FB3rfx++eWXZ1kkkbZK+tU3OUYAgfAKZPw2nO9AeItHyRBAAAEE0kngl19+cUPOFcSsU6dOrrOuQKWGqpcvX978ogO5PjmHAzUUX9dWT1QNqc/IyMjhDHYjgAACCCCAQFgFNNXO+vXrTT0/GzVqZOXKlctVUWmr5IqJgxBAAIFCEyAAWmi0XBgBBBBAAAEEEEAAAQQQQAABBBBAAAEEiluARZCKuwa4PwIIIIAAAggggAACCCCAAAIIIIAAAggUmgAB0EKj5cIIIIAAAggggAACCCCAAAIIIIAAAgggUNwCBECLuwa4PwIIIIAAAggggAACCCCAAAIIIIAAAggUmgAB0EKj5cIIIIAAAggggAACCCCAAAIIIIAAAgggUNwCBECLuwa4PwIIIIAAAggggAACCCCAAAIIIIAAAggUmgAB0EKj5cIIIIAAAggggAACCCCAAAIIIIAAAgggUNwCpYo7A9wfAQQQQCD1BNatW2cjR45MmLEyZcpYxYoV7cgjj7QLLrjA6tatm/A4NuZP4JtvvrH69evn7yKcjQACCCCAQBoLPPnkk/bBBx9kW4Jy5cq5Nknjxo3t3HPPTbu/m1dddZX9+uuv9tBDD1nVqlWzLWdwR79+/dyvo0ePtgoVKrjv1157re3cudPuu+8+q1mzZvBwviOAAAII/H8BAqD8p4AAAgggkEVg9+7d9tVXX2XZHtywePFie+GFF+zss8+2wYMHW6VKlYK7+Z6kwL59++ypp56yGTNm2OzZs5O8CqchgAACCCCQ/gLfffddju0RlfK9995zfzfvvPNOa926ddoUfPny5bZnzx4XBM1tpn37TO0Fn3SdHTt22N69e/0mPhFAAAEE4gQIgMaB8CsCCCCAQGaBJ554wtTrU+nAgQOuh8HmzZtdj4xXX33VZs2a5Rrct99+e+YT+S0pgW3bttnkyZOtVCn+RCcFyEkIIIAAAqET6Nq1q6m3ZDCpTbJlyxZbs2aNTZw40f7zn//YDTfcYP/85z8jNzpFPgqk+h6hQSe+I4AAAgj8nwBPV/yXgAACCCBwUIFGjRpZ2bJlsxxz+umn21lnneUeNubMmWOnnXaanXHGGVmOYwMCCCCAAAIIIJAfgdKlS7uh7vHX0OgTTcVz/PHH21/+8hdTj1GNUOnWrVv8oaH+/eqrrw51+SgcAgggUBACBEALQpFrIIAAAhEV0APHFVdcYeol+sADD9gpp5ximo8rPm3dutVWrlxpmtdSDypNmjRJONfVL7/8Yupdesghh1jlypVNvSGXLl3qejW0atXKqlevHru0ejp8+eWXtn79ejv88MPt2GOPtRIlEq/tpyFhq1atcr1ESpYsaZorTPNr5tTLUvn55JNPbNeuXe74Y445xjIyMmJ50Bf1PNE1jzjiCNuwYYPLb8uWLbP0Ptm4caN9/fXX9u2331qVKlVcnhs0aGB6qPNJx+jhTUk9W3RtJR2XXdncAfyDAAIIIIBAhAXKly9vJ510kv3rX/+yjz76KGEANLdtETGuXbvW/d3V33bN0alh56tXr3bza6rto/vFp4P9zdbUQvr7rnZHdvN7++mHNA+75llXW8mPwIm/V/zval/s378/27aN9q1YscKVQ3OENm/e3GrUqBF/Gfe78qH2mn7UDqtXr55r46htFp+CTmq3KO/qiat2TtOmTV17Lv4cfkcAAQSKS4AAaHHJc18EEEAgJAJ//OMf7R//+IfpwUINcDV4fdL8VM8995w988wzFpyrSsG8yy67zPr06ZMpCKnh9GPHjrWhQ4fajz/+6Ia0qdHuk3p0DBkyxPXu0JB7Tfjvkxrzmvw/GCTVPvUE0UIBasgHkx5qRowYYQpqxicFazWPmIKmwXT00UfbLbfc4gKofvvll1/uHiKuu+46u+2229wDiPYNGjTIevbs6cqhfCVaxEHBYF1PwV2lZ5991j286bu8ZKT0xhtvMKzNSfAPAggggAACiQX0QlQpftRKXtsiusaAAQNc+0TtBw2r18tZn/SCdvjw4Xbqqaf6Te7T/81+7bXX3Evc4E4FCrV4kf7uT506NbjLfVd7Qy+Sf/jhh9g+LYqkto4Cuzkl5VdzgE6bNs0OPfTQTIePGTPGlCcFM31SYPVPf/qT9e/fP9OL3VdeecV0vK4VTHr5e+GFF5p6mgZ9dV8Fg8ePH++cfBDYn9u7d2/3olwvikkIIIBAcQsQAC3uGuD+CCCAQJoLqIGuwKDe+KvhGwyAquH+zjvvWLVq1VwwsGHDhi4QqQa6gqI6XoHG+DRlyhTXU7JDhw6m3pTvv/+++9HCQOrNqYa8epvqoUAPCy+99JItW7bMNcBvuumm2OXUe9P/3rFjR2vbtq3rWak8vfvuu24+Ma28esIJJ8TO+f777+3GG2+0TZs2uSH97dq1cw8Cr7/+uiuj9ingG1xl1a+8qgcK9dhQT5E2bdq4oLCG5Ck4rCCn8queFEuWLHELNqg3yK233mrTp093DxSaQqBWrVpuESQFia+55hqXr9z2AIkVgi8IIIAAAghESGDhwoX26aefuhIfd9xxmUqebFtEoz+0urperCrwp8DiokWLTG2UkSNHmlaoV7umINLNN99stWvXNr1MVTtAizqpraM2xz333GNqiySTlEctWKkyDBw40PUQ/fDDD01TF+kFtUah9O3b1116woQJ9vTTT7sROl26dHHtr59//tnlRaNx1AY77LDD7JJLLsmUFbWBND+reoBqVJDafGrnzJs3z92jTp06ppflJAQQQKC4BQiAFncNcH8EEEAgBAI+GKgeDj6p56UCjRpipUZ1cKjVeeed53pCvP32265nZHzvBvXW1MOGb2R3797d7r33XtOiSzNnzrRevXq5hry/lwKwesDRPX1S74W7777b/ep7Y/p95557rmvkK18KgKrnpQ8yKmCq4KceCNS70yc9DKjHqnq5vvzyy5n2KcCphwL16tB8ZBo+ph4Szz//vAt+HnXUUfbII4+4ofK6noKx6k1ywQUXmIbZK8Dbvn17N4eZ5lzVKvAKgPbo0cPfnk8EEEAAAQQiK6D2hdoAwaSpcPTSUn+XFQBV0qiOzp07xw7LT1tEQ98VjNQ0P+r1qaSXmQroqe2gXqAKIhZE70YFVxWs1EtlJb0A1kiVcePGufvrpW9ep8LxAUi1vxTY9G01tTf0cllBXC0YpdEqurYCnErq8Rqc0109Rf/+97+7l80akeLbZu7g3/5RAFTD43UPPw2S2jf+HPUqJQDqtfhEAIHiFCAAWpz63BsBBBAIiYCCfkp+/kp9nzRpkj5coDAY/NQ2NfA7derkHhwUNIwPgKqRHt/A1kOHf/jxw8x0LaUWLVq4z//973+uh6jm2FJQUQ9GeoBIFEjUNdSrU0Pm9OCkxr56k+ohSz0lFGQNJj3gqGfGm2++6ebvDO7T9z//+c8u+KnvfniY5hrV8LLWrVtneUCSye9+9zv7/PPPXZBU55EQQAABBBBAIKuA5vXUT3ZJw7AVZNOQ7GBAMj9tEd1LLz598NPfW/dR4FAva/18nX5fsp964eqDn/4aaru8+OKLbrTMZ599Fpsux+/P6dO/FL700ktjwU9/jhax1DUrVqzohsbrJbDyoGHyweCnP15BU4220QvfREltKh/89Pv9OZofnYQAAgikggAB0FSoBfKAAAIIpLmAAo9KvneBviuQqKReBRqKHp/8seq5EZ80R1Z80kJHSjqvQoUKmXb7AKuGX2muLwVA/f01vD34MORP1DEaJqeGuc+DFghQ0nyiPojpj9fniSee6H6C2/x3BVrjk+4dHF6vvOl+emDSAk4+YKztJAQQQAABBBBILKCRHnqZqKSemfr7qQCf5gnv2rWrXX/99ZnmFPdX8W2BZNoiusbJJ5/sLxX7VJtCL17193z58uVuwaLYziS/qH0RnxSUVI9WTdmjdoqfLzz+uOx+922aROdpTk/19AwmzfEZTAp2qr2ixZ80JF8pu/ZKojaQhvQraeoiEgIIIJAKAgRAU6EWyAMCCCCQ5gJa2VzJBy7VaNbQbqX777/ffWb3jxY70jA2PwRdx2l4WXzyq6/73qbB/X5fcJsPaia6lj/O59cvnOAfFuIXEPDHH+zTXyv+GPVC1fxbH3/8sXuQCD4IJArMxp/P7wgggAACCERdQEO2NTVOMH3xxRcuiKdpaRQU1aKCwZTftojaJf4Fa/C6+u6De1o08ZxzzonfnaffD3Yf3x7x7ZTcXlhtDb8gkc9rTucquKmpiTRkXYsyBRd+yqm94l9qB++h+UWV9HKahAACCKSCAAHQVKgF8oAAAgiksYDmu/SrliYKAmrSffW2zEsKBkP9eXltQPt7BgOO/lr+U3lX8g17v+L8wc7x58Z/JsqzFkPSAgqaH0v50QJRzZo1M83zefzxx7uVVv28ZfHX43cEEEAAAQQQyF5A08hoIcW//vWvbsGgBg0amIZ7J0rJtEUO1u7QAklKVapUSXS7LNsUoM0uZderUsf78xK9/M3uen67b9Mc7Pr+WJVVlnPnznWbNPephsKrvaJ2i4bKa6Gj7FJe5yfN7jpsRwABBApTIG9PpIWZE66NAAIIIJCWAloNVY1sPQQoqKek+bI0l5V6gWpYl4auFXXyQ+YPNveUemcqac5PJa3QrqReqYmSAqazZ892x/myJjrOb9MiTAp+aq6tYcOGZRlW7/PmH1L8eXwigAACCCCAQM4CamNoocRp06a5BYPU3vBTz+S3LaLg408//ZSwF6h/8asXmz4pCKi/54leovopb/yxwU8FKNVbNX6uUR3j2ym+TRM872Df9dJVizPqvmrTaJGi+PTpp5+aVnnXMPtly5a54KcCnVp0Mn7YvHqGKtFeiVfkdwQQSCeBEumUWfKKAAIIIJBaAgrg+QUGLrroItMiBD4deeSR7qtvNPvt/vPf//63W2hIPQ4KI2nldSX1sPQ9PYP30UT/foEA39D3edawOj2MxKelS5e6If2PPfZY/K4sv2/ZssXNm6UdV1xxRZbgpwKjWkBBKdg7w/eiOFjPE3cS/yCAAAIIIICAqXdn/fr13VBrTbvje2eKxv9dT7Yt4ue+DDLr7/uSJUvcpuALXt9LU0HT+KR5NA+WfHskeIyuoyClUpMmTYK7cvW9YcOG7rhFixYlPF4rzN92222mYfz+Pm3bts0S/NTJmutUKdhecRv4BwEEEEgjAQKgaVRZZBUBBBBIBQE1yP/73//a5MmTrW/fvi64qAcP9cAIJq0mqqQeor5h7ferR8OYMWPcnJgHm6PTH5/M56mnnuoeGDZt2mRjx47N1GhX74zRo0e7IKceEHxvEfXk0Ir027dvdz1Jgj0dFLB86qmnXFbOPPPMHLOkRZT80Hr/oORPUkBWPUL90DbNgeqTX3xJDxl+cSm/j08EEEAAAQQQyCygv5tDhw51G/Vidvz48bED8tsWmTBhQmyaH11U7Qe1X9Qm0BDxatWqxe7lFwLSOcGkhSA1F/jB0uOPP+4WVfLH6D6PPPKIa2N16tTJ/LX9/tx89u7d2x2m3rF+PlB/3ltvveUWY9ToHbWB/OKSetEb34NVAVS1+ZSC7RV/LT4RQACBdBFgCHy61BT5RAABBIpJIDi5v3olBoOCypICiA8//HCWebC0wrpWZtXiBIMHD7bTTz/dzSWlXgQffvihe3jQsKvs5uvKb3HVk1IrnA4ZMsReeukl19DXaq7Kv3paaIJ/PVCMGjUq0xylmrNTCy3MnDnTtCiSAqIK2L7zzjsuz1qIoUePHjlmTw9kHTt2tDlz5rgArILAOlf3Vfk1JE0BV80TGgx0ai5RLSagwK0e3LQAgoajJRq+lmMmOAABBBBAAIEICGgkR5cuXdzf7hdffNFNPaO5K/PbFtFUPhrFoalsFCxUj1D93W7evLmNGDEik6xWUdcIkvnz51ufPn1cT0r9zdc29axM1JtUF9BwdbVZrrzySjvttNPckPsFCxa4npn626/tySTNkerbYf3793dl0IJIas8oX1pAUj1A1e7Qi129sFbbxOdDixip7aJFHNUDVeeod61G0Pjersnki3MQQACB4hIgAFpc8twXAQQQSBOB+OFOmqNKDXIteHTeeeeZelr6no7xRbrxxhvt2GOPNfVsmDdvnvvRMWpsq8eoAnzBYfPx5+f39xYtWtjEiRPtgQcecA14P4RLk/srsKthc/ErvCqgq3M0jO6DDz5w82IpHyqj8qwHIb/AUk75UwBWDxCzZs1ygVAFQ3Wd1q1buzypN+2gQYPs3XffdYsl+dXsb731VrcYgYKgmp9r7dq1BEBzwmY/AggggECkBfQ3XdPe6G/nfffd50Zt6O91ftoiDz74oHuJOX36dGerHp9q+yhI6EdsePSzzz7b9Z7UqBMNedeP2jgKQupFcHajR9ROUDtp+PDhLoCr6ykgqqCpRov4ecr9ffLyqbIrWKvh7q+++mrsVLV1rrvuutgIGE0bpCmJ1OtUbSXfXlJ59VK4W7dubrEpBUMVQD3//PNj1+ILAgggkC4CGb/15jmQLpklnwgggAAC6SugXhTr1q1zk/xr2Hv8g0Nhl0zDtr7++mv3MJLbxQR0joKPehDRMP9k86z5RL/99lsX/NQqtYlWjE9Ufj3E6eFNC0qREEAAAQQQQCB/ArltiyiYqWHuM2bMML001QtLjdZo3LixaxPklAv9zd+xY4cpsJjdS+JE19B9NIxf55UrVy7RIUlv27hxo1sUSW0wlSlR0ktvLfCkY/Wim9EniZTYhgAC6SpAADRda458I4AAAggggAACCCCAAAIIFLhAfAC0wG/ABRFAAAEEilyARZCKnJwbIoAAAggggAACCCCAAAIIIIAAAggggEBRCRAALSpp7oMAAggggAACCCCAAAIIIIAAAggggAACRS7AIkhFTs4NEUAAAQQQQAABBBBAAAEEUlWgffv2pnnAk537O1XLRb4QQACBKAswB2iUa5+yI4AAAggggAACCCCAAAIIIIAAAgggEHIBhsCHvIIpHgIIIIAAAggggAACCCCAAAIIIIAAAlEWIAAa5dqn7AgggAACCCCAAAIIIIAAAggggAACCIRcgABoyCuY4iGAAAIIIIAAAggggAACCCCAAAIIIBBlAQKgUa59yo4AAggggAACCCCAAAIIIIAAAggggEDIBQiAhryCKR4CCCCAAAIIIIAAAggggAACCCCAAAJRFiAAGuXap+wIIIAAAggggAACCCCAAAIIIIAAAgiEXIAAaMgrmOIhgAACCCCAAAIIIIAAAggggAACCCAQZQECoFGufcqOAAIIIIAAAggggAACCCCAAAIIIIBAyAUIgIa8gikeAggggAACCCCAAAIIIIAAAggggAACURYgABrl2qfsCCCAAAIIIIAAAggggAACCCCAAAIIhFyAAGjIK5jiIYAAAggggAACCCCAAAIIIIAAAgggEGUBAqBRrn3KjgACCCCAAAIIIIAAAggggAACCCCAQMgFCICGvIIpHgIIIIAAAggggAACCCCAAAIIIIAAAlEWIAAa5dqn7AgggAACCCCAAAIIIIAAAggggAACCIRcgABoyCuY4iGAAAIIIIAAAggggAACCCCAAAIIIBBlAQKgUa59yo4AAggggAACCCCAAAIIIIAAAggggEDIBQiAhryCKR4CCCCAAAIIIIAAAggggAACCCCAAAJRFiAAGuXap+wIIIAAAggggAACCCCAAAIIIIAAAgiEXIAAaMgrmOIhgAACCCCAAAIIIIAAAggggAACCCAQZQECoFGufcqOAAIIIIAAAggggAACCCCAAAIIIIBAyAUIgIa8gikeAggggAACCCCAAAIIIIAAAggggAACURYgABrl2qfsCCCAAAIIIIAAAggggAACCCCAAAIIhFyAAGjIK5jiIYAAAggggAACCCCAAAIIIIAAAgggEGUBAqBRrn3KjgACCCCAAAIIIIAAAggggAACCCCAQMgFCICGvIIpHgIIIIAAAggggAACCCCAAAIIIIAAAlEWIAAa5dqn7AgggAACCCCAAAIIIIAAAggggAACCIRcgABoyCuY4iGAAAIIIIAAAggggAACCCCAAAIIIBBlAQKgUa59yo4AAggggAACCCCAAAIIIIAAAggggEDIBQiAhryCKR4CCCCAAAIIIIAAAggggAACCCCAAAJRFiAAGuXap+wIIIAAAggggAACCCCAAAIIIIAAAgiEXIAAaMgrmOIhgAACCCCAAAIIIIAAAggggAACCCAQZQECoFGufcqOAAIIIIAAAggggAACCCCAAAIIIIBAyAUIgIa8gikeAggggAACCCCAAAIIIIAAAggggAACURYgABrl2qfsCCCAAAIIIIAAAggggAACCCCAAAIIhFyAAGjIK5jiIYAAAggggAACCCCAAAIIIIAAAgggEGUBAqBRrn3KjgACCCCAAAIIIIAAAggggAACCCCAQMgFCICGvIIpHgIIIIAAAggggAACCCCAAAIIIIAAAlEWIAAa5dqn7AgggAACCCCAAAIIIIAAAggggAACCIRcgABoyCuY4iGAAAIIIIAAAggggAACCCCAAAIIIBBlAQKgUa59yo4AAggggAACCCCAAAIIIIAAAggggEDIBQiAhryCKR4CCCCAAAIIIIAAAggggAACCCCAAAJRFiAAGuXap+wIIIAAAggggAACCCCAAAIIIIAAAgiEXIAAaMgrmOIhgAACCCCAAAIIIIAAAggggAACCCAQZQECoFGufcqOAAIIIIAAAggggAACCCCAAAIIIIBAyAUIgIa8gikeAggggAACCCCAAAIIIIAAAggggAACURYgABrl2qfsCCCAAAIIIIAAAggggAACCCCAAAIIhFyAAGjIK5jiIYAAAggggAACCCCAAAIIIIAAAgggEGUBAqBRrn3KjgACCCCAAAIIIIAAAggggAACCCCAQMgF/h8hLRQgOjOqdQAAAABJRU5ErkJggg==" width="672" /></p>
<pre class="r"><code>####
houseContrastP &lt;- cces2.2
houseContrastP &lt;- subset(houseContrastP, c(Pres_Vote == &quot;Solid Democrat&quot; | Pres_Vote == &quot;Solid Republican&quot;))

houseContrastP$pid &lt;- factor(houseContrastP$pid)
houseContrastPD &lt;- subset(houseContrastP, pid==&quot;Democrat&quot;)
houseContrastPR &lt;- subset(houseContrastP, pid==&quot;Republican&quot;)

houseContrastPD &lt;- summarySE(houseContrastPD, measurevar=&quot;RCO327_p&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
houseContrastPD$office &lt;- &quot;u.s. house&quot;
houseContrastPD$party &lt;- &quot;Democrat&quot;
houseContrastPD$proportion &lt;- houseContrastPD$RCO327_p
houseContrastPD$RCO327_p &lt;- NULL


houseContrastPR &lt;- summarySE(houseContrastPR, measurevar=&quot;RCO327_p&quot;, groupvars=c(&quot;Pres_Vote&quot;), na.rm=TRUE)
houseContrastPR$office &lt;- &quot;u.s. house&quot;
houseContrastPR$party &lt;- &quot;Republican&quot;
houseContrastPR$proportion &lt;- houseContrastPR$RCO327_p
houseContrastPR$RCO327_p &lt;- NULL

houseContrastPDR &lt;- rbind(houseContrastPD, houseContrastPR)
houseContrastPDR$Pres_Vote &lt;- ordered(houseContrastPDR$Pres_Vote, 
                                      levels = c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;))

pd &lt;- position_dodge(0.1)
houseCDP &lt;- ggplot(houseContrastPDR, aes(x=Pres_Vote, y=proportion, colour=party, group=party, legend=&quot;right&quot;)) + 
  geom_errorbar(aes(ymin=proportion-ci, ymax=proportion+ci), colour=&quot;black&quot;, width=.1, position=pd) +
  geom_line(position=pd) +
  geom_point(position=pd, size=3, shape=21, fill=&quot;white&quot;) + 
  xlab(&quot;&quot;) +
  ylab(&quot;&quot;) +
  scale_color_manual(values=c(&quot;Democrat&quot; = &quot;blue&quot;, 
                              &quot;Republican&quot; = &quot;red&quot;)) +   
  scale_x_discrete(breaks=c(&quot;Solid Democrat&quot;, &quot;Solid Republican&quot;),
                   labels=c(&quot;Solid\nDemocrat&quot;, &quot;Solid\nRepublican&quot;)) + 
  ggtitle(&quot;U.S. House&quot;) +
  ylim(0,1) +                        
  theme_bw() 

houseCDP</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAYAAAD0ZtPZAAAEGWlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VQNcC+8AAAA4ZVhJZk1NACoAAAAIAAGHaQAEAAAAAQAAABoAAAAAAAKgAgAEAAAAAQAABUCgAwAEAAAAAQAAA8AAAAAAthHDUQAAQABJREFUeAHs3QeYXVW9N+A1aaRBGoQECL0EkQ5SpQUBr1cEBLlcsaGIIhelKegnTUUQr4DIxUIRpYMoAqEEKdGAIkVq6L2GFAIhIW3mm/+GczwzmUlmJvvM2TPz7uc5nF3XXvtdeWbCL2vtVdfQuCQLAQIECBAgQIAAAQIECBAgQIAAAQIEuqFAr274TB6JAAECBAgQIECAAAECBAgQIECAAAECmYAA1B8EAgQIECBAgAABAgQIECBAgAABAgS6rYAAtNs2rQcjQIAAAQIECBAgQIAAAQIECBAgQEAA6s8AAQIECBAgQIAAAQIECBAgQIAAAQLdVkAA2m2b1oMRIECAAAECBAgQIECAAAECBAgQICAA9WeAAAECBAgQIECAAAECBAgQIECAAIFuKyAA7bZN68EIECBAgAABAgQIECBAgAABAgQIEBCA+jNAgAABAgQIECBAgAABAgQIECBAgEC3FRCAdtum9WAECBAgQIAAAQIECBAgQIAAAQIECAhA/RkgQIAAAQIECBAgQIAAAQIECBAgQKDbCghAu23TejACBAgQIECAAAECBAgQIECAAAECBASg/gwQIECAAAECBAgQIECAAAECBAgQINBtBQSg3bZpPRgBAgQIECBAgAABAgQIECBAgAABAn0QdD2Bd999Nz3xxBPlim+44Yapb9++5e22rDz11FPpnXfeyU5dYYUV0pgxY9pyWZvPmTdvXpo4cWJ64IEH0pQpU9LMmTPT8OHD08iRI9NGG22Udthhh9SvX782l5fXiXPmzEmTJ08uF7fBBhukZZZZprzdlpVq27WlDs4hQIAAAQIECBAgQIAAAQIECBBom0BdQ+PStlOdVRSBSZMmpe23375cnVdffTWNHj26vN2WlZ133jndcccd2amHHnpoOuecc9py2RLPefPNN9Pxxx+fLr744jRr1qxWzx88eHDab7/90o9+9KN2173VQttw4KGHHkobb7xx+czHH388rbfeeuXttqxUy64t93YOAQIECBAgQIAAAQIECBAgQIBA+wQMgW+fl7MXI3DbbbelddZZJ/3yl79cbPgZRUQ4euGFF2bh45VXXrmYUh0iQIAAAQIECBAgQIAAAQIECBAg0HEBQ+A7bufKCoEHH3ww7bXXXuVh9XFoyy23TOPGjUsrr7xyimH2MRT+xRdfTBMmTEhxfiwxDP/AAw9MQ4cOTbvttlu2z38IECBAgAABAgQIECBAgAABAgQI5CUgAM1LsoeXc8wxx5TDz3jXZ/TqjPCzpSXeunDttdemL37xi9m7QefPn5++/OUvp2eeeaYm7wVtqY72ESBAgAABAgQIECBAgAABAgQIdA8BQ+C7RzvW9CliQqbo1VlaFhd+xjl1dXVZb9GLLrooW499L7/8crrkkkti1UKAAAECBAgQIECAAAECBAgQIEAgNwEBaG6UPbeg0nD2EIjJmFrr+dlc6FOf+lT66Ec/Wt596623ltetECBAgAABAgQIECBAgAABAgQIEMhDwBD4PBR7eBlTp04tCwwZMqS83paVj33sY2nixIlZT9DoBdodl9deey3de++96bHHHktPPvlkWnHFFdNGG22UfcaOHZt69Vr8v0P861//Sm+99VZG86EPfSiNHDmyVab6+vrMs3RCBMy9e/cubTb5/uc//5niE68emDFjRlp33XVTlB+ftdZaq9w7t8lFrWzEu1wjCI/PQw89lL3TdZNNNknxWW+99Zb4jK0UazcBAgQIECBAgAABAgQIECBAYKkFBKBLTaiAVVddtYwQAV8EfRGitWU54ogj0te+9rU0bNiwVoO6tpRTxHPefffd9OMf/zj97//+b3rvvfdarOIWW2yR4lUAi/MKozvuuCO7/rLLLkv/9V//1WJZsXPOnDlp5513Lh+PYHLw4MHl7Vj5+9//no466qh01113NdlfufHhD384/fSnP02777575e4W13/5y19m5c2ePbvF49tss026+OKL05prrtnicTsJECBAgAABAgQIECBAgAABAtUUWHzXs2reWdndRmCHHXZIyy23XPY80QMxhrbfdNNNbXq+QYMGpeWXX77bhZ9PPfVU1vPxRz/6UZPwM8LIeAdqaYmeoZtttlkWkpb2VfP70UcfTXvssUeT8LNfv35p1KhRqU+ff/97yCOPPJI+8YlPpGuuuabV6rz55pvZOV//+tdTZfgZfxYqy7r77rvTxhtvnH73u9+1WpYDBAgQIECAAAECBAgQIECAAIFqCQhAqyXbg8qNwCtmgS8tTz/9dPr4xz+eNt9883TiiSemCMAWLlxYOtztv+NZv/CFL6RXXnkle9Zlllkm600ZvWPffvvtbLj5Lbfckg0PjxPmzp2bjj766PTnP/+56jb77LNPmjlzZnaf/fffP+utG71TY5h+fN92221pgw02yI7Hc5x00kmt1umggw5K48ePz45HiBrnPv/881n50fM0epiWepDOmjUrfelLX0r3339/q+U5QIAAAQIECBAgQIAAAQIECBCohsC/u3xVo3Rl9hiB733veylmg4+hzqUlwq74RDA2dOjQtOOOO6addtop+8Q7MJf07stSOdX8jt6QEd61Z3nppZcWe/rpp5+ehb5xUryvM3rDbrrppuVr4j2p8e7TCIYjRIxh7bEcfvjhadddd00DBw4sn5vnynPPPZe9gzTKjPpceumlTdog3hUaw+fPP//8tPXWW2e3jvd5PvDAA03qHwfima6//vrsnAh4J02alAXe2Y7G//Tv3z/F0PcISGO4/Zlnnpmid/A3v/nN9Ne//rV0mm8CBAgQIECAAAECBAgQIECAQNUFBKBVJ+4ZN4hh3b///e+zAO073/lOqpwYKQRiEp9rr702+8T2SiutlD7zmc+kL3/5yyneN1mrJXos5rm8+uqr6YQTTigXefzxxy8SHpYORkh47rnnpptvvjlNnz49vfDCC9k7Q3/wgx+UTsn1O4bbl5ZVVlmlSfhZ2h/fW221VfrqV7+aTY4UkyHFu0wrlwULFqR4L2lp+eEPf9gk/Cztj+8IuSMQvuGGG1K8FuBvf/tbuvLKK7O2rzzPOgECBAgQIECAAAECBAgQIECgWgKGwFdLtoeWGz0aYzb36F0Y7wJddtllW5SIoDB6BcYs4dHzMYaGd4clZmyfN29e9iirrbZaOvjggxf7WNEb9Nhjjy2fE2FotZZ4D2dpiSH4f/rTn0qbi3z/6le/SrfeemuK7+23377J8Zjp/fHHH8/2xbs+v/GNbzQ53nwjzok/F6Wl1HO0tO2bAAECBAgQIECAAAECBAgQIFBNAT1Aq6nbQ8uOIdEHHHBA9onegjHU+y9/+Uu6/fbbsxnISwFh8MR7Js8+++z0j3/8Izun+Yzl1SaMd162d3bymIU9ZlJvaZk8eXJ593bbbdem4fW77LJL+Zp4jUC1lnXXXTetvvrqKXq9xntH99577xQTWH3yk5/M3tW54YYbtunW0ZOztESAPWDAgNJmq9/xPtjSUnl9aZ9vAgQIECBAgAABAgQIECBAgEC1BASg1ZKtYrnxrsbKpSMTDFWGkM3Lqyx7adej999HP/rR7BMTIs2ZMyfrWRjDoC+//PIUAWks99xzTzYc/oorrljaW7br+hiKHz0127PE0PXWlsoANMLGtixrrLFG+bToCRsTEo0ePbq8L8+VeL9n5URIEydOTPGJSaxWXnnlbPKq//iP/8i+W3vOygDzsccea5Pf/Pnzy49ReX15pxUCBAgQIECAAAECBAgQIECAQJUEDIGvEmw1i40JhSqXmHG7vcvrr79eviSGYXfWEr0Fo8dhvC80ejvGZEil5aqrrkqVAWJpf1f6Lg0Njzq3NQAdPnx4quz5+swzz1TtkaO3aUxYNHbs2EXuEbPWn3feeVlAuuKKK2bv+awMyksXVAaYs2fPTi+++OISPxHqlpZp06Zl74QtbfsmQIAAAQIECBAgQIAAAQIECFRTQA/QaupWqewIzCqXjrw/szKQah6oVpa9pPWYICdmF3/jjTdSQ0NDNov5kq4pHY+h5/EeytIQ9Lj+xhtvTOuvv37plC73HcP/S8t7771XWl3sd/TgrTx3aWaBD8MlLRtssEEWNJcmJIr3jj755JNNLos/U/GO1nh9wR/+8Iesd2jphJjNvbREW3VkEqu21LN0D98ECBAgQIAAAQIECBAgQIAAgaUREIAujV6Nrh02bFiTO8ekQzFzd1uXmTNnZkPRS+c3D1RL+9vyfdNNN6V99903OzWGTE+ZMqXViY9aKi+Gf2+xxRapNEN5NXs/tnT/vPets846Kd4rGku8a7MtS/S8LL0KIM4fOXJkq5dVntfSSe0Jw2Nyo9IERxFiRxAaAfSECRPKfz7i3azf/va30yWXXFK+XbxLtLRsuummTY6V9vsmQIAAAQIECBAgQIAAAQIECBRFwBD4orREO+rRt2/ftN5665WvuOuuu8rrbVmJmcorlwixOrpU9taMXozXXXddu4uqfN9lWybUafcNOvGCCEBLS1sD0MrzevXqlVZYYYVSEdl35TtaWxqSXnny1KlTKzfbvB5B9Ne+9rV07bXXpgjU99tvv/K1EYhW9tisDEAfeOCB8nlWCBAgQIAAAQIECBAgQIAAAQJFFBCAFrFV2lCnXXfdtXzWNddc02QIdflAKys/+clPykdi+HvlezjLB9q4EoFfZY/Fyp6CbSki3l8aAVtpqQx2S/u60ndlABrP1ZZA8uKLLy4/4rbbbpsqh9HHgcrJiJZUXmuz00c5Z5xxRhZsxpD1Ui/V2N98iR7Bv/nNb1KEsbG8+eab6dFHHy2fVhmAxntc29Jr97LLLktbbrlldv+YDMtCgAABAgQIECBAgAABAgQIEOgsAQFoZ0nnfJ899tijXGL0IDz22GPL24tbid6i48ePL58SExKVgq7yznasRG/Ub33rW+UrouwTTjihvL24lZgZ/Lvf/W45vI13X+69996Lu6Twxz72sY+lVVddNatnhLunnnrqYusc4eGFF15YPqey52Vp56hRo0qr6c9//nOT3pjlA40r06dPTz/72c8qdzVZjwmarr766izMvPLKK5sca74R7/ms7PVZ+Z7YTTbZJJV6Dcd5hx12WPPLm2zHsP0IPeM1B3H/eE2ChQABAgQIECBAgAABAgQIECDQWQIC0M6Szvk+//mf/5l22mmncqlnnXVW9i7OylnIywcbV2bNmpW+853vNLlm0KBB6ZRTTqk8bZH1c845JwspI6j87W9/u8jx2HHooYem1VZbrXzs5JNPzt4tGe+TbKnHYgRrDz30UIrejr/4xS/K1339619Pyy+/fHm7tNKWOpTOrfV3DOGvDCHPPvvs9Ktf/arFaj377LNZm5Xe67nyyiunz33uc4ucWxl2xwzu55577iLnxOsH9txzzxQ9MltbPvOZz5QPRQ/PaJ/WlmjDUgAarzmIupWWCMyj3erq6rJd8R7Y4447rsl7TEvnRhlf/OIXy5MsxTWHH3546bBvAgQIECBAgAABAgQIECBAgEDVBeoaA4olTxtd9Wq4QUcEYubuGFbcfOKbjTfeOK211lophjLH+xwjaIteopXvj4wQK0Kwgw46aLG33nzzzdP999+fnbP77runCLtaWiJ43W677bJeiM2PjxkzJm222WYpJm+aPHlyeuyxx1L0jqxcYkh/9B6NHqXNl7bWofl1LW1H8Bo+pSXq3d5h9zvvvHO64447siIi/I2AtvkSz/OXv/ylvPtTn/pUFlBGz8nXXnstxeRCEVrHhFSxRHvE+TtVhNqliyMgjYD51VdfzXaF0Y477pii9260cQy1v+WWW9Lrr7+eorfokCFDykFoOA8ePDi7LnprfuITnyi3YdzzwAMPTPvss0+Kd4DGn4/4sxJ/Lm699dbsmj59+mSzwEe42nyJYPOiiy4q7452ij9P0UM0hvE//PDD2UzyDz74YPmcCNwjLLUQIECAAAECBAgQIECAAAECBDpLoE9n3ch98heIdzHGsOIIsB555JHyDSJwqgydygc+WIlA7PLLL8/CsObHOro9duzYNHHixHTIIYek6KVYubz00kspPi0t0SMwgrQIA1sKP1u6pivs++Mf/5iOPPLIdN5552XVjcmF4tPSEpMexXtAWwo/4/wIIX/9619nYeVbb72V4tUBEVCWQspSmWuuuWYWhMaQ9JZ6gkbgGe2+zTbbZEF0BKK/+93vsk+pjMrv6I171VVXtVqvqNNKK62U4p2yCxcuTPfdd1/2qSyjcv2YY44RflaCWCdAgAABAgQIECBAgAABAgQ6RcAQ+E5hrt5NYtKd6E0Yw6K333778rDklu4YvQh/+MMfpqeeeirX8LN0rw022CD99a9/TZdeemnW2zGG2Le2RAgbQ7LvvPPOdMEFF6Rll122tVO75P54nuhJGb1aN9xww1Q5k3vpgSJgPOqoo9K//vWvtNtuu5V2t/gdPTdjxvVx48YtEhRHz9q99tor/e1vf8t6/rZYwAc7o3doBJVnnnlmk2HtlddEL9LosfrPf/6z1fAzzu/Xr1/2CoW77747C1WbT95UKjOG8Mc5lZNvlY75JkCAAAECBAgQIECAAAECBAhUW8AQ+GoLd3L5b7zxRjbcPYZDx3oEcausskoWdq2++upLNeFRex9l7ty5Wc/UmEU8PrNnz04rrrhiGj16dDYMvXJ28/aW3dXOj3d0xvD/mE09guEIo2M29ggR27uEawzlj1cgxHD+CJ5L7+NsT1kxtD565r744ovZJ4bTx6sKon06skQv0AjXozdylB2vYYheqSNGjOhIca4hQIAAAQIECBAgQIAAAQIECOQiIADNhVEhBAgQIECAAAECBAgQIECAAAECBAgUUcAQ+CK2ijoRIECAAAECBAgQIECAAAECBAgQIJCLgAA0F0aFECBAgAABAgQIECBAgAABAgQIECBQRAEBaBFbRZ0IECBAgAABAgQIECBAgAABAgQIEMhFQACaC6NCCBAgQIAAAQIECBAgQIAAAQIECBAoooAAtIitok4ECBAgQIAAAQIECBAgQIAAAQIECOQiIADNhVEhBAgQIECAAAECBAgQIECAAAECBAgUUUAAWsRWUScCBAgQIECAAAECBAgQIECAAAECBHIREIDmwqgQAgQIECBAgAABAgQIECBAgAABAgSKKCAALWKrqBMBAgQIECBAgAABAgQIECBAgAABArkICEBzYVQIAQIECBAgQIAAAQIECBAgQIAAAQJFFBCAFrFV1IkAAQIECBAgQIAAAQIECBAgQIAAgVwEBKC5MCqEAAECBAgQIECAAAECBAgQIECAAIEiCghAi9gq6kSAAAECBAgQIECAAAECBAgQIECAQC4CAtBcGBVCgAABAgQIECBAgAABAgQIECBAgEARBQSgRWwVdSJAgAABAgQIECBAgAABAgQIECBAIBcBAWgujAohQIAAAQIECBAgQIAAAQIECBAgQKCIAgLQIraKOhEgQIAAAQIECBAgQIAAAQIECBAgkIuAADQXRoUQIECAAAECBAgQIECAAAECBAgQIFBEAQFoEVtFnQgQIECAAAECBAgQIECAAAECBAgQyEWgTy6lKKRTBBYuXJieffbZTrmXmxAg0LUFGhoayg9QV1dXXrdCgAABAgS6q0Dl7754Rr//umtLey4C+Qkss8wyadVVV82vQCURIFBYgbrGvyj8+/+SC1tNFQuBadOmpW233RYGAQIECBAgQIAAAQIECBAgsJQCa621Vho/fvxSluJyAgS6goAeoF2hlZrV8bDDDksf+chHmu21SYAAgX8LzJgxI9XX12e9X4YPH/7vA9YIECBAgEA3FViwYEGaOXNm9nT9+vVLyy67bDd9Uo9FgEAeAhdddFF6/vnn8yhKGQQIdAEBAWgXaKTmVVx77bXTVltt1Xy3bQIECJQF3njjjXIAOmrUqPJ+KwQIECBAoLsKzJ8/P02dOjV7vP79+6dhw4Z110f1XAQI5CBw4403CkBzcFQEga4iYBKkrtJS6kmAAAECBAgQIECAAAECBAgQIECAQLsFBKDtJnMBAQIECBAgQIAAAQIECBAgQIAAAQJdRUAA2lVaSj0JECBAgAABAgQIECBAgAABAgQIEGi3gAC03WQuIECAAAECBAgQIECAAAECBAgQIECgqwgIQLtKS6knAQIECBAgQIAAAQIECBAgQIAAAQLtFhCAtpvMBQQIECBAgAABAgQIECBAgAABAgQIdBUBAWhXaSn1JECAAAECBAgQIECAAAECBAgQIECg3QIC0HaTuYAAAQIECBAgQIAAAQIECBAgQIAAga4iIADtKi2lngQIECBAgAABAgQIECBAgAABAgQItFtAANpuMhcQIECAAAECBAgQIECAAAECBAgQINBVBASgXaWl1JMAAQIECBAgQIAAAQIECBAgQIAAgXYLCEDbTeYCAgQIECBAgAABAgQIECBAgAABAgS6ioAAtKu0lHoSIECAAAECBAgQIECAAAECBAgQINBuAQFou8lcQIAAAQIECBAgQIAAAQIECBAgQIBAVxEQgHaVllJPAgQIECBAgAABAgQIECBAgAABAgTaLSAAbTeZCwgQIECAAAECBAgQIECAAAECBAgQ6CoCAtCu0lLqSYAAAQIECBAgQIAAAQIECBAgQIBAuwUEoO0mcwEBAgQIECBAgAABAgQIECBAgAABAl1FQADaVVpKPQkQIECAAAECBAgQIECAAAECBAgQaLeAALTdZC4gQIAAAQIECBAgQIAAAQIECBAgQKCrCAhAu0pLqScBAgQIECBAgAABAgQIECBAgAABAu0WEIC2m8wFBAgQIECAAAECBAgQIECAAAECBAh0FQEBaFdpKfUkQIAAAQIECBAgQIAAAQIECBAgQKDdAgLQdpO5gAABAgQIECBAgAABAgQIECBAgACBriIgAO0qLaWeBAgQIECAAAECBAgQIECAAAECBAi0W6BPu6/oQhfMnj07HX300WnUqFHp+OOP71DNH3/88XTVVVelF154IQ0aNChtuOGGaZdddklrrrlmq+V15JpWC3OAAAECBAgQIECAAAECBAgQIECAAIEOC3TbALShoSGdfPLJ6eGHH059+nTsMa+++up01llnZbiDBw9O8+bNS/fff3+68sor06mnnpo222yzReA7cs0ihdhBgAABAgQIECBAgAABAgQIECBAgEAuAt1yCPycOXPSaaedliZNmtRhpAhOf/7zn6d+/fqlH/3oR2n8+PHppptuSocffniK8qNn6euvv96k/I5c06QAGwQIECBAgAABAgQIECBAgAABAgQI5CrQ7QLQe++9N33+859PN9xwQ+rVq+OPd9FFF6XoRXrggQemHXbYIdXV1aW+ffum/fbbL+27775p/vz56U9/+lOTxujINU0KsEGAAAECBAgQIECAAAECBAgQIECAQK4CHU8Ic61GPoVNmDAhHXHEEVnPzK222iodeeSRHSo43h16zz33ZNfuvvvui5RR2nf99denBQsWZMc7cs0iBdtBgAABAgQIECBAgAABAgQIECBAgECuAt0qAJ0+fXpaaaWV0rHHHptOP/30NGzYsA5hTZ48Oev9OWbMmKy85oWMHTs2LbvssmnmzJnpxRdfzA535Jrm5domQIAAAQIECBAgQIAAAQIECBAgQCBfgY7NDpRvHXIrbdy4cenTn/50hyc9KlXklVdeyVaHDh1a2rXIdxx755130ksvvZTNCN+RaxYptHHHnXfemS688MKWDqX+/ftn+2fNmpWmTZvW4jl2EiBAIATq6+sziHiVh58X/kwQIECAQE8QKP3ui2eNyUv9/usJre4ZCXRc4L333iv/nbnjpbiSAIGuItCtAtDll18+F/d33303K2dxAehyyy2XnVM6t/Tdnmtaquwbb7yR7r777pYOlXu0xrD7+EudhQABAm0R8POiLUrOIUCAAIHuJBBhqN9/3alFPQuB/AXi50R0FrAQINAzBLrVEPi8mize5xlLDHNvbRk8eHB2KP7VKJaOXJNd6D8ECBAgQIAAAQIECBAgQIAAAQIECFRNoFv1AM1LadCgQVlRi/tX47lz52bnLLPMMtl3R65pqb6f/OQn04477tjSoeydo3E8ep+OHDmyxXPsJECAQAhMnTo1G9JTV1eXVlhhBSgECBAgQKDbC8yfPz/NmDEje874O/qQIUO6/TN7QAIEOi4wYMCA1Lt3744X4EoCBLqUgAC0heYqDaV/++23Wzj6/q54/2cspeCzI9e8X1LT/8YP4fi0tPTp835z9erVyw/qloDsI0CgRQF/sWuRxU4CBAgQ6GYCle8AjX8A9PuvmzWwxyGQs0D8nLAQINBzBAyBb6GtS2FmKeRs4ZRUCkdLM8135JqWyrWPAAECBAgQIECAAAECBAgQIECAAIH8BASgLViWhpfHDO8xlKb5MnPmzDR9+vQUPTHXWWed7HBHrmlerm0CBAgQIECAAAECBAgQIECAAAECBPIVEIC24LnSSiulsWPHplmzZqV//OMfi5xx++23p4ULF2bnDBw4MDvekWsWKdgOAgQIECBAgAABAgQIECBAgAABAgRyFejxAeikSZPShAkT0nPPPdcE9oADDsi2L7zwwlQ5FH7KlCnpsssuy47tt99+S31NkwJsECBAgAABAgQIECBAgAABAgQIECCQq0CPnwTprLPOSq+99lo6+OCD0xprrFHGjZnY119//TR58uT0la98Je28885pwYIF6dZbb03Tpk1L2223Xdpll13K58dKR65pUoANAgQIECBAgAABAgQIECBAgAABAgRyFejxAWhrmjFr5Nlnn53OOOOMdMstt6RLLrkkOzX277vvvumQQw7J3gFaeX1Hrqm83joBAgQIECBAgAABAgQIECBAgAABAvkK1DU0LvkW2f1Ki56fzzzzTAqqMWPGpEGDBi3xITtyzZIKjZ6n2267bTrzzDPTxz/+8SWd7jgBAj1Y4I033kj19fWprq4ujRo1qgdLeHQCBAgQ6CkCMXnp1KlTs8ft379/GjZsWE95dM9JgEAHBE488cR0zz33pPHjx3fgapcQINDVBPQAbUOL9enTJ6233nptOPPfp3Tkmn9fbY0AAQIECBAgQIAAAQIECBAgQIAAgTwEevwkSHkgKoMAAQIECBAgQIAAAQIECBAgQIAAgWIKCECL2S5qRYAAAQIECBAgQIAAAQIECBAgQIBADgIC0BwQFUGAAAECBAgQIECAAAECBAgQIECAQDEFBKDFbBe1IkCAAAECBAgQIECAAAECBAgQIEAgBwEBaA6IiiBAgAABAgQIECBAgAABAgQIECBAoJgCAtBitotaESBAgAABAgQIECBAgAABAgQIECCQg4AANAdERRAgQIAAAQIECBAgQIAAAQIECBAgUEwBAWgx20WtCBAgQIAAAQIECBAgQIAAAQIECBDIQUAAmgOiIggQIECAAAECBAgQIECAAAECBAgQKKaAALSY7aJWBAgQIECAAAECBAgQIECAAAECBAjkICAAzQFREQQIECBAgAABAgQIECBAgAABAgQIFFNAAFrMdlErAgQIECBAgAABAgQIECBAgAABAgRyEBCA5oCoCAIECBAgQIAAAQIECBAgQIAAAQIEiikgAC1mu6gVAQIECBAgQIAAAQIECBAgQIAAAQI5CAhAc0BUBAECBAgQIECAAAECBAgQIECAAAECxRQQgBazXdSKAAECBAgQIECAAAECBAgQIECAAIEcBASgOSAqggABAgQIECBAgAABAgQIECBAgACBYgoIQIvZLmpFgAABAgQIECBAgAABAgQIECBAgEAOAgLQHBAVQYAAAQIECBAgQIAAAQIECBAgQIBAMQUEoMVsF7UiQIAAAQIECBAgQIAAAQIECBAgQCAHAQFoDoiKIECAAAECBAgQIECAAAECBAgQIECgmAIC0GK2i1oRIECAAAECBAgQIECAAAECBAgQIJCDgAA0B0RFECBAgAABAgQIECBAgAABAgQIECBQTAEBaDHbRa0IECBAgAABAgQIECBAgAABAgQIEMhBQACaA6IiCBAgQIAAAQIECBAgQIAAAQIECBAopoAAtJjtolYECBAgQIAAAQIECBAgQIAAAQIECOQgIADNAVERBAgQIECAAAECBAgQIECAAAECBAgUU0AAWsx2USsCBAgQIECAAAECBAgQIECAAAECBHIQEIDmgKgIAgQIECBAgAABAgQIECBAgAABAgSKKSAALWa7qBUBAgQIECBAgAABAgQIECBAgAABAjkICEBzQFQEAQIECBAgQIAAAQIECBAgQIAAAQLFFBCAFrNd1IoAAQIECBAgQIAAAQIECBAgQIAAgRwEBKA5ICqCAAECBAgQIECAAAECBAgQIECAAIFiCghAi9kuakWAAAECBAgQIECAAAECBAgQIECAQA4CAtAcEBVBgAABAgQIECBAgAABAgQIECBAgEAxBQSgxWwXtSJAgAABAgQIECBAgAABAgQIECBAIAcBAWgOiIogQIAAAQIECBAgQIAAAQIECBAgQKCYAgLQYraLWhEgQIAAAQIECBAgQIAAAQIECBAgkIOAADQHREUQIECAAAECBAgQIECAAAECBAgQIFBMAQFoMdtFrQgQIECAAAECBAgQIECAAAECBAgQyEFAAJoDoiIIECBAgAABAgQIECBAgAABAgQIECimgAC0mO2iVgQIECBAgAABAgQIECBAgAABAgQI5CAgAM0BUREECBAgQIAAAQIECBAgQIAAAQIECBRTQABazHZRKwIECBAgQIAAAQIECBAgQIAAAQIEchAQgOaAqAgCBAgQIECAAAECBAgQIECAAAECBIopIAAtZruoFQECBAgQIECAAAECBAgQIECAAAECOQgIQHNAVAQBAgQIECBAgAABAgQIECBAgAABAsUUEIAWs13UigABAgQIECBAgAABAgQIECBAgACBHAQEoDkgKoIAAQIECBAgQIAAAQIECBAgQIAAgWIKCECL2S5qRYAAAQIECBAgQIAAAQIECBAgQIBADgIC0BwQFUGAAAECBAgQIECAAAECBAgQIECAQDEFBKDFbBe1IkCAAAECBAgQIECAAAECBAgQIEAgBwEBaA6IiiBAgAABAgQIECBAgAABAgQIECBAoJgCAtBitotaESBAgAABAgQIECBAgAABAgQIECCQg4AANAdERRAgQIAAAQIECBAgQIAAAQIECBAgUEwBAWgx20WtCBAgQIAAAQIECBAgQIAAAQIECBDIQUAAmgOiIggQIECAAAECBAgQIECAAAECBAgQKKaAALSY7aJWBAgQIECAAAECBAgQIECAAAECBAjkICAAzQFREQQIECBAgAABAgQIECBAgAABAgQIFFNAAFrMdlErAgQIECBAgAABAgQIECBAgAABAgRyEBCA5oCoCAIECBAgQIAAAQIECBAgQIAAAQIEiikgAC1mu6gVAQIECBAgQIAAAQIECBAgQIAAAQI5CAhAc0BUBAECBAgQIECAAAECBAgQIECAAAECxRQQgBazXdSKAAECBAgQIECAAAECBAgQIECAAIEcBASgOSAqggABAgQIECBAgAABAgQIECBAgACBYgoIQIvZLmpFgAABAgQIECBAgAABAgQIECBAgEAOAgLQHBAVQYAAAQIECBAgQIAAAQIECBAgQIBAMQUEoMVsF7UiQIAAAQIECBAgQIAAAQIECBAgQCAHAQFoDoiKIECAAAECBAgQIECAAAECBAgQIECgmAIC0GK2i1oRIECAAAECBAgQIECAAAECBAgQIJCDgAA0B0RFECBAgAABAgQIECBAgAABAgQIECBQTAEBaDHbRa0IECBAgAABAgQIECBAgAABAgQIEMhBQACaA6IiCBAgQIAAAQIECBAgQIAAAQIECBAopoAAtJjtolYECBAgQIAAAQIECBAgQIAAAQIECOQgIADNAVERBAgQIECAAAECBAgQIECAAAECBAgUU0AAWsx2USsCBAgQIECAAAECBAgQIECAAAECBHIQEIDmgKgIAgQIECBAgAABAgQIECBAgAABAgSKKdCnmNVSq8UJzJs3L82ePXtxpzhGgEAPF2hoaMgE4tvPix7+h8HjEyBAoIcILFy4sPykse73X5nDCgECLQgsWLAglf7O3MJhuwgQ6GYCAtAu1KClH87xl7mZM2d2oZqrKgECtRTw86KW+u5NgAABArUQmD9/vr8v1wLePQl0IYHoWFRfX9+FaqyqBAgsjYAAdGn0Ovnaurq67I5Dhw5No0aN6uS7ux0BAl1JYMqUKdlf6OLnxoorrtiVqq6uBAgQIECgQwIRek6bNi27tn///in+zmwhQIBAawIDBgxIvXv3bu2w/QQIdDMBAWgXbdBSGNpFq6/aBAh0ooCfF52I7VYECBAgUDOB5r/vmm/XrGJuTIBAIQX8jChks6gUgaoJmASparQKJkCAAAECBAgQIECAAAECBAgQIECg1gIC0Fq3gPsTIECAAAECBAgQIECAAAECBAgQIFA1AQFo1WgVTIAAAQIECBAgQIAAAQIECBAgQIBArQUEoLVuAfcnQIAAAQIECBAgQIAAAQIECBAgQKBqAgLQqtEqmAABAgQIECBAgAABAgQIECBAgACBWgsIQGvdAu5PgAABAgQIECBAgAABAgQIECBAgEDVBASgVaNVMAECBAgQIECAAAECBAgQIECAAAECtRYQgNa6BdyfAAECBAgQIECAAAECBAgQIECAAIGqCQhAq0arYAIECBAgQIAAAQIECBAgQIAAAQIEai0gAK11C7g/AQIECBAgQIAAAQIECBAgQIAAAQJVExCAVo1WwQQIECBAgAABAgQIECBAgAABAgQI1FpAAFrrFnB/AgQIECBAgAABAgQIECBAgAABAgSqJiAArRqtggkQIECAAAECBAgQIECAAAECBAgQqLWAALTWLeD+BAgQIECAAAECBAgQIECAAAECBAhUTUAAWjVaBRMgQIAAAQIECBAgQIAAAQIECBAgUGsBAWitW8D9CRAgQIAAAQIECBAgQIAAAQIECBComoAAtGq0CiZAgAABAgQIECBAgAABAgQIECBAoNYCAtBat4D7EyBAgAABAgQIECBAgAABAgQIECBQNQEBaNVoFUyAAAECBAgQIECAAAECBAgQIECAQK0FBKC1bgH3J0CAAAECBAgQIECAAAECBAgQIECgagIC0KrRKpgAAQIECBAgQIAAAQIECBAgQIAAgVoLCEBr3QLuT4AAAQIECBAgQIAAAQIECBAgQIBA1QQEoFWjVTABAgQIECBAgAABAgQIECBAgAABArUWEIDWugXcnwABAgQIECBAgAABAgQIECBAgACBqgkIQKtGq2ACBAgQIECAAAECBAgQIECAAAECBGotIACtdQu4PwECBAgQIECAAAECBAgQIECAAAECVRMQgFaNVsEECBAgQIAAAQIECBAgQIAAAQIECNRaQABa6xZwfwIECBAgQIAAAQIECBAgQIAAAQIEqiYgAK0arYIJECBAgAABAgQIECBAgAABAgQIEKi1gAC01i3g/gQIECBAgAABAgQIECBAgAABAgQIVE1AAFo1WgUTIECAAAECBAgQIECAAAECBAgQIFBrAQForVvA/QkQIECAAAECBAgQIECAAAECBAgQqJqAALRqtAomQIAAAQIECBAgQIAAAQIECBAgQKDWAgLQWreA+xMgQIAAAQIECBAgQIAAAQIECBAgUDUBAWjVaBVMgAABAgQIECBAgAABAgQIECBAgECtBQSgtW4B9ydAgAABAgQIECBAgAABAgQIECBAoGoCAtCq0SqYAAECBAgQIECAAAECBAgQIECAAIFaCwhAa90C7k+AAAECBAgQIECAAAECBAgQIECAQNUEBKBVo1UwAQIECBAgQIAAAQIECBAgQIAAAQK1FhCA1roF3J8AAQIECBAgQIAAAQIECBAgQIAAgaoJCECrRqtgAgQIECBAgAABAgQIECBAgAABAgRqLSAArXULuD8BAgQIECBAgAABAgQIECBAgAABAlUTEIBWjVbBBAgQIECAAAECBAgQIECAAAECBAjUWkAAWusWcH8CBAgQIECAAAECBAgQIECAAAECBKomIACtGq2CCRAgQIAAAQIECBAgQIAAAQIECBCotYAAtNYt4P4ECBAgQIAAAQIECBAgQIAAAQIECFRNQABaNVoFEyBAgAABAgQIECBAgAABAgQIECBQawEBaK1bwP0JECBAgAABAgQIECBAgAABAgQIEKiagAC0arQKJkCAAAECBAgQIECAAAECBAgQIECg1gIC0Fq3gPsTIECAAAECBAgQIECAAAECBAgQIFA1AQFo1WgVTIAAAQIECBAgQIAAAQIECBAgQIBArQUEoLVuAfcnQIAAAQIECBAgQIAAAQIECBAgQKBqAgLQqtEqmAABAgQIECBAgAABAgQIECBAgACBWgsIQGvdAu5PgAABAgQIECBAgAABAgQIECBAgEDVBASgVaNVMAECBAgQIECAAAECBAgQIECAAAECtRYQgNa6BdyfAAECBAgQIECAAAECBAgQIECAAIGqCQhAq0arYAIECBAgQIAAAQIECBAgQIAAAQIEai0gAK11C7g/AQIECBAgQIAAAQIECBAgQIAAAQJVExCAVo1WwQQIECBAgAABAgQIECBAgAABAgQI1FpAAFrrFnB/AgQIECBAgAABAgQIECBAgAABAgSqJiAArRqtggkQIECAAAECBAgQIECAAAECBAgQqLWAALTWLeD+BAgQIECAAAECBAgQIECAAAECBAhUTUAAWjVaBRMgQIAAAQIECBAgQIAAAQIECBAgUGsBAWitW8D9CRAgQIAAAQIECBAgQIAAAQIECBComoAAtGq0CiZAgAABAgQIECBAgAABAgQIECBAoNYCAtBat4D7EyBAgAABAgQIECBAgAABAgQIECBQNQEBaNVoFUyAAAECBAgQIECAAAECBAgQIECAQK0FBKC1bgH3J0CAAAECBAgQIECAAAECBAgQIECgagIC0KrRKpgAAQIECBAgQIAAAQIECBAgQIAAgVoLCEBr3QLuT4AAAQIECBAgQIAAAQIECBAgQIBA1QQEoFWjVTABAgQIECBAgAABAgQIECBAgAABArUWEIDWugXcnwABAgQIECBAgAABAgQIECBAgACBqgkIQKtGq2ACBAgQIECAAAECBAgQIECAAAECBGotIACtdQu4PwECBAgQIECAAAECBAgQIECAAAECVRMQgFaNVsEECBAgQIAAAQIECBAgQIAAAQIECNRaQABa6xZwfwIECBAgQIAAAQIECBAgQIAAAQIEqiYgAK0arYIJECBAgAABAgQIECBAgAABAgQIEKi1gAC01i3g/gQIECBAgAABAgQIECBAgAABAgQIVE1AAFo1WgUTIECAAAECBAgQIECAAAECBAgQIFBrgT61rkDe9587d266+uqr07333ptmzJiR1llnnbTJJpukPfbYI/Xu3btNt3vkkUfSLbfc0qZzt91227T11luXz7399tvTQw89VN5uvrL88sunz372s8132yZAgAABAgQIECBAgAABAgQIECBAoAoC3SoAfeutt9Khhx6aXnrppYxq+PDh6aabbso+d911VzrhhBNSv379lsj4/PPPpz/+8Y9LPC9OGDFiRJMA9Nprr0333Xdfq9euueaaAtBWdRwgQIAAAQIECBAgQIAAAQIECBAgkK9AtwpAf/CDH2Th51ZbbZW+//3vpyFDhqRXXnklfe9730sTJ05MP//5z9PRRx+9RMGNNtooHXPMMa2eFz08b7755jRw4MC00047NTnvqaeeyrYPP/zwtMwyyzQ5FhvLLrvsIvvsIECAAAECBAgQIECAAAECBAgQIECgOgLdJgB97LHH0j333JMGDBiQfvjDH6b+/ftnYiuvvHL62c9+lvbZZ5904403pkMOOWSJIeSqq66a4tPSMm3atHT++ednhyJkXW211cqnTZkyJb399ttZr9D99tuvvN8KAQIECBAgQIAAAQIECBAgQIAAAQK1Eeg2kyDdcccdmeCOO+5YDj9LpDEU/iMf+UiaN29eFoKW9nfkO3qZTp8+Pe29995p++23b1JEqffneuut12S/DQIECBAgQIAAAQIECBAgQIAAAQIEaiPQbQLQRx99NBOM4e8tLRGAxrK4CYpauq5y34QJE7L3e0ag+tWvfrXyULbePABdsGBBFpYucqIdBAgQIECAAAECBAgQIECAAAECBAh0ikC3GQIf7/qMZejQoS3ClfaXJkhq8aTF7JwzZ04655xzsjNioqXBgwcvcnYpAI3g84gjjkgPPPBAWrhwYTbkfsstt0zxXtCYNGlxyxNPPJEmTZrU4ilRVizvvfdemjVrVovn2EmAAIEQaGhoyCDi288LfyYIECBAoCcIlP6uHM8afx/3+68ntLpnJNBxgfnz55f/ztzxUlxJgEBXEeg2Aei7776bmZeCzuYNsNxyy2W7Suc1P76k7ej9Ge//jABz3LhxLZ7+9NNPZ/t///vfp969e6exY8dmf/l69tln02233ZbuvffedNZZZ6W11167xetj54MPPphOO+20Fo8PGzYs2x8B6DvvvNPiOXYSIECguYCfF81FbBMgQIBAdxeIANTvv+7eyp6PwNIJRABaX1+/dIW4mgCBLiPQLQLQ+KEVoWAsrc2yXuqxOXfu3A41zp///Ofsuk996lOpT59F2SJYffXVV7Nzdt9993TUUUdlEzLFjth/0kknpZio6ZRTTkm//vWvWywju9h/CBAgQIAAAQIECBAgQIAAAQIECBDITWDRJC+3ojuvoF69emVhYwxTby3gLO3v169fuysWQ9tjaHr06txzzz1bvD5mn7/00kvT1KlT0yabbJLq6urK56200krp5JNPTp/97GdTlBWz1W+77bbl45UrW2yxRYqJllpa4l+yI0gdOHBgGjJkSEun2EeAAIFM4O233y4P6fHzwh8KAgQIEOgJAjEEvjTsvW/fvtnfmXvCc3tGAgQ6JhDZQGQJFgIEeoZAtwhAo6mWX375FO/3bG2oS2n/oEGD2t2y1113XXZNzDDf2js84wfnmDFjsk9LN1hxxRXThhtumA2DjyHxrQWga665ZopPS0sMwY8ANH5QRwhqIUCAQGsC8TMv3v8Z/xjj50VrSvYTIECAQHcSiOGspQA0Oi74/dedWtezEMhfIEZ2VnZcyv8OSiRAoEgC3eafOyIAjaUUdDZHjt5QsZTeo9n8eGvbMbT+lltuyQ7vs88+rZ3Wpv0jR47Mzosg00KAAAECBAgQIECAAAECBAgQIECAQPUFuk0AWgoXo3dlS0tp//rrr9/S4Vb3xXD1eL/n6NGj08Ybb9zqeTFE/qKLLkpXXXVVq+dMmTIlO7bKKqu0eo4DBAgQIECAAAECBAgQIECAAAECBAjkJ9BtAtDSzOy33nrrIjoxSVLMwh5LvJ+zPcsjjzySnb7OOuss9rIZM2ak8847L5199tnphRdeWOTc6dOnp0cffTTbv8EGGyxy3A4CBAgQIECAAAECBAgQIECAAAECBPIX6DYB6NZbb51WX331bJKhG2+8sYnUJZdckmLY+WqrrZa22mqrJscmTZqUJkyYkJ577rkm+0sbMXN7LGussUZpV4vfEawOHTo0e+feb3/72xQTFpWWGEZ/2mmnpZikabvttktjx44tHfJNgAABAgQIECBAgAABAgQIECBAgEAVBbrNJEjx8uKDDz44HX/88emUU05Jd999d4pemw8//HC2HjNBfvvb317kJcdnnXVWeu2117JrWwo5X3nllYy/tYmJSm3Tv3//dMIJJ6QjjzwyRS/U++67L+22227ZzPETJ05ML7/8chaiHnHEEaVLfBMgQIAAAQIECBAgQIAAAQIECBAgUGWBbhOAhtMOO+yQzjjjjCwAvf3221N8YomeoRE8brTRRtl2W/8TQ+djaHssLYWjzcvZYost0rnnnpsiVJ08eXK64oorslMGDBiQdt999ywcNRtlczXbBAgQIECAAAECBAgQIECAAAECBKon0K0C0GDadNNNs4mIYsj7Sy+9lGJypFGjRqVevVoe7X/llVe2qhvX3HHHHa0eb+lAvN/z17/+dZo5c2Z2/2WXXTaNGTOm1fu3VIZ9BAgQIECAAAECBAgQIECAAAECBAjkI9DtAtASy4gRI1J8arUMGTIkxcdCgAABAgQIECBAgAABAgQIECBAgEDtBFruFlm7+rgzAQIECBAgQIAAAQIECBAgQIAAAQIEchMQgOZGqSACBAgQIECAAAECBAgQIECAAAECBIomIAAtWouoDwECBAgQIECAAAECBAgQIECAAAECuQkIQHOjVBABAgQIECBAgAABAgQIECBAgAABAkUTEIAWrUXUhwABAgQIECBAgAABAgQIECBAgACB3AQEoLlRKogAAQIECBAgQIAAAQIECBAgQIAAgaIJCECL1iLqQ4AAAQIECBAgQIAAAQIECBAgQIBAbgIC0NwoFUSAAAECBAgQIECAAAECBAgQIECAQNEEBKBFaxH1IUCAAAECBAgQIECAAAECBAgQIEAgNwEBaG6UCiJAgAABAgQIECBAgAABAgQIECBAoGgCAtCitYj6ECBAgAABAgQIECBAgAABAgQIECCQm4AANDdKBREgQIAAAQIECBAgQIAAAQIECBAgUDQBAWjRWkR9CBAgQIAAAQIECBAgQIAAAQIECBDITUAAmhulgggQIECAAAECBAgQIECAAAECBAgQKJqAALRoLaI+BAgQIECAAAECBAgQIECAAAECBAjkJiAAzY1SQQQIECBAgAABAgQIECBAgAABAgQIFE1AAFq0FlEfAgQIECBAgAABAgQIECBAgAABAgRyExCA5kapIAIECBAgQIAAAQIECBAgQIAAAQIEiiYgAC1ai6gPAQIECBAgQIAAAQIECBAgQIAAAQK5CQhAc6NUEAECBAgQIECAAAECBAgQIECAAAECRRMQgBatRdSHAAECBAgQIECAAAECBAgQIECAAIHcBASguVEqiAABAgQIECBAgAABAgQIECBAgACBogkIQIvWIupDgAABAgQIECBAgAABAgQIECBAgEBuAgLQ3CgVRIAAAQIECBAgQIAAAQIECBAgQIBA0QQEoEVrEfUhQIAAAQIECBAgQIAAAQIECBAgQCA3AQFobpQKIkCAAAECBAgQIECAAAECBAgQIECgaAIC0KK1iPoQIECAAAECBAgQIECAAAECBAgQIJCbgAA0N0oFESBAgAABAgQIECBAgAABAgQIECBQNAEBaNFaRH0IECBAgAABAgQIECBAgAABAgQIEMhNQACaG6WCCBAgQIAAAQIECBAgQIAAAQIECBAomoAAtGgtoj4ECBAgQIAAAQIECBAgQIAAAQIECOQmIADNjVJBBAgQIECAAAECBAgQIECAAAECBAgUTUAAWrQWUR8CBAgQIECAAAECBAgQIECAAAECBHITEIDmRqkgAgQIECBAgAABAgQIECBAgAABAgSKJiAALVqLqA8BAgQIECBAgAABAgQIECBAgAABArkJCEBzo1QQAQIECBAgQIAAAQIECBAgQIAAAQJFExCAFq1F1IcAAQIECBAgQIAAAQIECBAgQIAAgdwEBKC5USqIAAECBAgQIECAAAECBAgQIECAAIGiCQhAi9Yi6kOAAAECBAgQIECAAAECBAgQIECAQG4CAtDcKBVEgAABAgQIECBAgAABAgQIECBAgEDRBASgRWsR9SFAgAABAgQIECBAgAABAgQIECBAIDcBAWhulAoiQIAAAQIECBAgQIAAAQIECBAgQKBoAgLQorWI+hAgQIAAAQIECCoMnu0AAEAASURBVBAgQIAAAQIECBAgkJuAADQ3SgURIECAAAECBAgQIECAAAECBAgQIFA0AQFo0VpEfQgQIECAAAECBAgQIECAAAECBAgQyE1AAJobpYIIECBAgAABAgQIECBAgAABAgQIECiagAC0aC2iPgQIECBAgAABAgQIECBAgAABAgQI5CYgAM2NUkEECBAgQIAAAQIECBAgQIAAAQIECBRNQABatBZRHwIECBAgQIAAAQIECBAgQIAAAQIEchMQgOZGqSACBAgQIECAAAECBAgQIECAAAECBIomIAAtWouoDwECBAgQIECAAAECBAgQIECAAAECuQkIQHOjVBABAgQIECBAgAABAgQIECBAgAABAkUTEIAWrUXUhwABAgQIECBAgAABAgQIECBAgACB3AQEoLlRKogAAQIECBAgQIAAAQIECBAgQIAAgaIJCECL1iLqQ4AAAQIECBAgQIAAAQIECBAgQIBAbgIC0NwoFUSAAAECBAgQIECAAAECBAgQIECAQNEEBKBFaxH1IUCAAAECBAgQIECAAAECBAgQIEAgNwEBaG6UCiJAgAABAgQIECBAgAABAgQIECBAoGgCAtCitYj6ECBAgAABAgQIECBAgAABAgQIECCQm4AANDdKBREgQIAAAQIECBAgQIAAAQIECBAgUDQBAWjRWkR9CBAgQIAAAQIECBAgQIAAAQIECBDITUAAmhulgggQIECAAAECBAgQIECAAAECBAgQKJqAALRoLaI+BAgQIECAAAECBAgQIECAAAECBAjkJiAAzY1SQQQIECBAgAABAgQIECBAgAABAgQIFE2gT9EqpD4ECBAgkI/AX/7yl3T99dengQMH5lPgB6Ust9xy6cwzz8y1TIURIECAAAECBAgQIECAAIFqCQhAqyWrXAIECNRY4LHHHktXXHFF7rUYOXKkADR3VQUSIECAAAECBAgQIECAQLUEDIGvlqxyCRAgQIAAAQIECBAgQIAAAQIECBCouYAeoDVvAhUgQIBAdQT23XfftM0226QRI0Ys9gbHHHNMuuuuu7JzosfoKqusstjz+/Xrt9jjDhIgQIAAAQIECBAgQIAAgSIJCECL1BrqQoAAgRwFRo8enVZaaaU0atSoxZY6fPjw8vHNNtssrb322uVtKwQIECBAgAABAgQIECBAoKsLGALf1VtQ/QkQIECAAAECBAgQIECAAAECBAgQaFVAANoqjQMECBAgQIAAAQIECBAgQIAAAQIECHR1AQFoV29B9SdAgAABAgQIECBAgAABAgQIECBAoFUBAWirNA4QIECAAAECBAgQIECAAAECBAgQINDVBQSgXb0F1Z8AAQIECBAgQIAAAQIECBAgQIAAgVYFBKCt0jhAgAABAgQIECBAgAABAgQIECBAgEBXFxCAdvUWVH8CBAgQIECAAAECBAgQIECAAAECBFoVEIC2SuMAAQIECBAgQIAAAQIECBAgQIAAAQJdXaBPV3+Anlj/+vr6tHDhwp746J6ZAIEOCCzp50VDQ0O51Dh3SeeXT7ZCgAABAgQKJFD5+yt+t1VuF6iaqkKAQEEEKv8OXJAqqQYBAlUUEIBWETfvoks/oN9+++00ZcqUvItXHgEC3VAgfm4s6efF3Llzy08+bdq0tNxyy5W3rRAgQIAAga4oEL/blvT7rys+lzoTIJCfwJw5c/xDSX6cSiJQeAEBaOGb6N8VrKuryzYGDhyYhg4d+u8D1ggQINBMYObMman0jyZL+nnRt2/f8tURfi7p/PLJVggQIECAQIEEFixYkGbNmpXVKH63DRo0qEC1UxUCBIom0K9fv9Srl7cCFq1d1IdAtQQEoNWSrWK58YN6wIABVbyDogkQ6OoC0VM8AtD4h5Ml/bzo3bt3+XH79++/xPPLJ1shQIAAAQIFEpg/f345AI3fbUv6/VegqqsKAQI1EOjTp0/2d+Ua3NotCRCogYB/7qgBulsSIECAAAECBAgQIECAAAECBAgQINA5AgLQznF2FwIECBAgQIAAAQIECBAgQIAAAQIEaiAgAK0BulsSIECAAAECBAgQIECAAAECBAgQINA5AgLQznF2FwIECBAgQIAAAQIECBAgQIAAAQIEaiAgAK0BulsSIECAAAECBAgQIECAAAECBAgQINA5AgLQznF2FwIECBAgQIAAAQIECBAgQIAAAQIEaiAgAK0BulsSIECAAAECBAgQIECAAAECBAgQINA5AgLQznF2FwIECBAgQIAAAQIECBAgQIAAAQIEaiAgAK0BulsSIECAAAECBAgQIECAAAECBAgQINA5AgLQznF2FwIECBAgQIAAAQIECBAgQIAAAQIEaiAgAK0BulsSIECAAAECBAgQIECAAAECBAgQINA5AgLQznF2FwIECBAgQIAAAQIECBAgQIAAAQIEaiAgAK0BulsSIECAAAECBAgQIECAAAECBAgQINA5AgLQznF2FwIECBAgQIAAAQIECBAgQIAAAQIEaiAgAK0BulsSIECAAAECBAgQIECAAAECBAgQINA5AgLQznF2FwIECBAgQIAAAQIECBAgQIAAAQIEaiAgAK0BulsSIECAAAECBAgQIECAAAECBAgQINA5AgLQznF2FwIECBAgQIAAAQIECBAgQIAAAQIEaiAgAK0BulsSIECAAAECBAgQIECAAAECBAgQINA5AgLQznF2FwIECBAgQIAAAQIECBAgQIAAAQIEaiAgAK0BulsSIECAAAECBAgQIECAAAECBAgQINA5AgLQznF2FwIECBAgQIAAAQIECBAgQIAAAQIEaiAgAK0BulsSIECAAAECBAgQIECAAAECBAgQINA5AgLQznF2FwIECBAgQIAAAQIECBAgQIAAAQIEaiAgAK0BulsSIECAAAECBAgQIECAAAECBHqKwF133dVTHtVzFlRAAFrQhlEtAgQIECBAgAABAgQIECBAgEBXFnjjjTfS5z73uTRu3Liu/Bjq3g0EBKDdoBE9AgECBAgQIECAAAECBAgQIECgaAInnnhiuvjii1NDQ0PRqqY+PUxAANrDGtzjEiBAgAABAgQIECBAgAABAgQIEOhJAgLQntTanpUAAQIECBAgQIAAAQIECBAgQIBADxPo08Oe1+MSIECAAAECBAgQIECAAAECBLqswIsvvphef/31NHjw4PShD30oG14+efLkNGnSpPTyyy+nNdZYI33sYx9LK6+88hKfcc6cOenJJ59Mjz/+eHriiSdS//790zrrrJN9ouxevRbtNzd//vz0wAMPZGXHOQMHDkwxydFtt92WRo8enfbZZ580d+7crC5TpkzJzosh8Pfcc0+2PmrUqNSnT5/seOxYb7310pAhQ7JjLf3n3XffTY8++mh2aK211kojRoxo6TT7CCxWQAC6WB4HCRAgQIAAAQIECBAgQIAAAQLFEfjZz36WzjrrrLTVVlul8ePHp0984hPp73//e5MK9uvXL33lK19JZ5xxRor1lpZf/epX6eijj06zZs1q6XBW/nnnnZc+/OEPNzk+bdq07FjsjND17LPPTpdffnn5nCjzW9/6Vjr55JPL++bNm1e+Jo7tueeeaZdddsmOH3/88emkk04qn9t85fzzz0/f/OY3s90R1gpAmwvZbouAALQtSs4hQIAAAQIECBAgQIAAAQIECBRIYPr06WmHHXbIekeuu+66abfddksLFixIt9xyS3r22WfT//3f/6Wnn3463XDDDVmPy1LVozdmhKY33nhjtmullVZKW2+9dRo+fHh69dVXs56aU6dOTf/4xz/SNttsk6LH6bBhw0qXN/n+zW9+0yT8jIPbbbddWm211dIWW2yRXnjhhfTmm2+murq6tPnmm2fXjhkzJu20005p9dVXT88//3y65JJLFhuA/u53v8uui3Kjd6qFQEcEenXkItcQIECAAAECBAgQIECAAAECBAjUTuCpp57Kws8vf/nL6ZFHHsl6Yp577rnZ+v77759VLMLQ2Fe5RPBZCj+/8Y1vZCHkH/7whxRhZoSlzz33XPriF7+YXRK9Q3/5y19WXt5k/be//W027D3uEcPizzzzzKxX6UEHHZT++c9/pk9/+tPZ+dELNbbjc+SRR2aB6Be+8IXs2DPPPJPuvvvuJuWWNh577LF03333ZZulOpWO+SbQHgEBaHu0nEuAAAECBAgQIECAAAECBAgQKIhADIOPYep9+/Yt12jAgAHp97//fdYLM3aecsop5WOxEsPnY1lllVWy9cprY3+8W/QXv/hFuddo9ARd3BIh6Ne+9rW0ySabZEPVS0PbF3dNHItAM3qGxnLxxRdn383/E88RSzzTZz7zmeaHbRNos4AAtM1UTiRAgAABAgQIECBAgAABAgQIFEfgxBNPbLEyEWoec8wx2bGYMCkmRyotp556arryyivTBRdckHr37l3a3eR70KBBWUAaO1t7R2gciwmMYuh9R5YYAh9D4WO54oorUkyuVLnU19eXg9G99947LbfccpWHrRNol4B3gLaLy8kECBAgQIAAAQIECBAgQIAAgdoLxAzt48aNa7Ui0Tu0tMTw9OjxGcumm26afUrHKr9nzpyZHnrooXTHHXekmOwolnivaGvL0r6T80tf+lK6/fbbs3vddNNN6ZOf/GT5VrG/FNwa/l5msdJBAQFoB+FcRoAAAQIECBAgQIAAAQIECBColUBMXtR8+HplXVZdddXy5r/+9a8m4WIciIAzeoI+/PDD2btEn3jiifTGG2+Ur2nLytprr92W01o9J94Rethhh6W333476+1ZGYCWJj+K4HZxQW+rhTtAoEJAAFqBYZUAAQIECBAgQIAAAQIECBAg0BUEVlxxxcVWs3LIeMzkXrl897vfzd7/OXv27Mrd2Xr06oxZ4i+99NI0ZcqURY5X7lh++eUrN9u9PnDgwOzdnvEe0+uuuy4LQqPe7777brrmmmuy8j73uc+l6O1qIbA0Av4ELY2eawkQIECAAAECBAgQIECAAAECNRBYUm/NV155pVyrFVZYobx+3HHHpR//+Mcpws/hw4enGIZ+zjnnpL/+9a/prbfeSk8++WQ644wzUoSTnbHE/WOZM2dOOfT84x//WH73qOHvndEK3f8eeoB2/zb2hAQIECBAgAABAgQIECBAgEA3E3jttdey93P26dNytFPZ6zMmHIolhr2fdtpp2fr666+fJkyYkFZeeeVsu/l/pk+fnu1auHBh80O5bm+77bbZZEoxBP8Pf/hDisAzAtBYttlmm7Tuuuvmej+F9UwBPUB7Zrt7agIECBAgQIAAAQIECBAgQKALC0QwGRMHtbbE+z1Ly8c//vFsdeLEiamhoSFbP+SQQ1oNPydPnpwNR48TFzcJUlbQYv5TV1eXHS3ds7VTS708b7311iykvfnmm7NTS/tbu85+Am0VEIC2Vcp5BAgQIECAAAECBAgQIECAAIECCZx00kmppR6azz33XLrggguymu6www5pzJgx2fq8efPKtX/mmWfK65UrMSHR5z//+fKu+fPnl9fbu9K/f//skrjvrFmzWr087te7d+/03nvvpW9/+9vZO0Dj2v3337/Vaxwg0B6BlvtJt6cE5xIgQIAAAQIECBAgQKAgAldddVWaOnVqGjBgQK412mSTTVKpB1WuBSuMAAECSyFw7733pj322CPFjOmjR4/OSrrzzjvTAQcckCJ0jOHxP//5z8t32HLLLVP0yowemRGQxizs22+/fRY+1tfXp5gt/vDDD09RbmmZMWNGabXd38OGDStfE2HtnnvumUaMGJE+9KEPlffHSsxov9tuu6Ubb7wxXXjhhdmxvfbaKw0ZMqTJeTYIdFRAANpROdcRIECAAAECBAgQIFA4gYsuuijdf//9udfr4IMPFoDmrqpAAgSWVmDzzTdPMWw8AsS11147RVgZ7/mMpW/fvilmV994443Lt1lzzTXT0UcfnU4//fSsl+VOO+2UBZIbbbRR9rNz5syZ2bnR8zImQYow8uWXX84mRxo6dGi5nLau7LjjjuXA9ac//WmKT4Sg11577SJFxGRIEYCWhssb/r4IkR1LIWAI/FLguZQAAQIECBAgQIAAAQIECBAgUCuB6O150EEHpRgu/vTTT5fDz5133jndcccdTYayl+p4yimnZEFkKdCMwPT222/P3vn54Q9/OAshL7/88vTZz342uyR6ksbkRB1ZYvj9L37xiyygLV0f7xdtaYlgNGaljyUmZtp1111bOs0+Ah0S0AO0Q2wuIkCAAAECBAgQIECgiAJHHnlk9j/xgwYNarV6McwzehrFEr2mfvzjH7d6bumAWYhLEr4JECiSQLzu4/zzz0/nnntuuu+++7L3bI4dO7b8zs+W6hrD4o866qj0jW98I8V7QON9oSNHjkwbbLBBqvzZOW7cuHJvzMpyRo0a1eL+ynMq1w899NAUn5i1fvbs2WmVVVapPFxej3eAlma0L70TtHzQCoGlFBCALiWgywkQIECAAAECBAgQKI7ALrvskvWEqnzvXPPaVQag0QOqcrKP5ufaJkCAQFcQ6NevX9pmm23aVdXoNRqhZ3w6Yym9o7S1e8Xw9ylTpmRD5qNXq4VAngKGwOepqSwCBAgQIECAAAECBAgQIECAAIF2CcRM8z/5yU+ya6LnabzP1EIgTwE9QPPUVBYBAgQIECBAgAABAgQIECBAgMASBe6666704IMPZrPVX3311elvf/tbds1xxx23xGudQKC9AgLQ9oo5nwABAt1EoOGBf6WG625Ix9z3YPqf1DfNa3yu4Sf8INXv/alUt8duqW7w4G7ypB6DAAECBAgQIECAAIGiCbz00kvZu0Er6/X9738/xatMLATyFhCA5i2qPAIECBRcoL4x9Kw/7v+l9Oj7sy9um9X3gzeiXHpFqm/8pP7LpLr/OTT1Ou7bqW7YsII/keoRIECAAAECBAgQ6DkCMQlRZ723s5qqMcx9mWWWSXPnzs0mbfrv//7vdNJJJ1XzlsruwQIC0B7c+B6dAIGeJdDw3nup/qCvpobLrnz/wbfYLPXad5+Uttw81TX+JSq9805qePa51DD+ptRwzZ9Sw+lnpIW/vzT1/tNVqW6rj/QsLE9LgAABAgQIECBAoKACxx57bIpPV18233zzbNb6119/vdWZ4bv6M6p/cQQEoMVpCzUhQIBA1QQi/Fy468dTmnR3Squtmnqdc1bq9YnG7WZLFnQesH9q+Mkpqf4730sNjQHowh13Tb2uuyb1+tiuzc62SYAAAQIECBAgQIAAgY4L9OnTR/jZcT5XtkPALPDtwHIqAQIEuqpA/cFffz/8bOzt2fufk1oMPyufrW706NT7dxekXmecntLceal+/wNTw9NPV55inQABAgQIECBAgAABAgQIdAkBAWiXaCaVJECAQMcF6m+6OTVcfFlKY1ZJva//Y6pbYYU2F9brW/+T6r5zVEoz3kr1hx3R5uucSIAAAQIECBAgQIAAAQIEiiIgAC1KS6gHAQIEqiRQ/93js5J7/fxnqW7kyHbfpdcPTkxp7bVSw80TUsOdE9t9vQsIECBAgAABAgQIECBAgEAtBQSgtdR3bwIECFRZoPeTT6X0wIMpbfTh1GuvPTt0t7q+fRtngz8muzabIb5DpbiIAAECBAgQIECAAAECBAjURkAAWht3dyVAgECnCPS79bbsPr322Wup7lcX4WnvXqnh+vFLVY6LCRAgQIAAAQIECBAgQIBAZwsIQDtb3P0IECDQiQK9n3jy/btttulS3bVu+PCUVl89pVdfSw0zZixVWS4mQIAAAQIECBAgQIAAAQKdKdCnM2/mXgQIECDQuQJ1b83Mbli3Yvvf/blITaOMZ55NC0evllK8S3TE8FQ3YkRKw4c1rjd+x3YEpY3fqfG7Lr5Lx4cNS3V9/MpZxNQOAgQIECBAgAABAgQIEKi6gP8brTqxGxAgQKCGAsssk9284e23U93SVmPm+2FqmjsvpZdezj4Nzcpsvt3k8NAhWTDaJCj9ICCtDEubhKpDhqS6uqWueZNq2CBAgAABAgQIECBAgACBniUgAO1Z7e1pCRDoYQILVx3z/hM//UxKu47r8NM3LFiQ0vMvpsY0MqVRK6Y0fXrKgtD2lBi9UePz7HOpeVDafLtcbK/G+zX2Hk3LRw/TEY09TBvXy6Hp+71Oo7dp896odYMGlYuwQoAAAQIECBAgQIAAAQI9W0AA2rPb39MTINDNBebvsH1K5/wyNdxwY0pf+2qHn7bhzokpvftuqvuP3VPvG67NymmYMyelqVNTmjY9NUQg2vidpk1rDEdnpIYPvmO7IfaXjr/1VkrzG8PUti71jdFoVm5jGempJsFpq6FplN238dfb8stnwWjz4fiVPUyznqelYfsRsPbr19aaOY8AAQIECBAgQIAAAQIEuoiAALSLNJRqEiBAoCMC87fZKntHZ8NNt6SGp55Odeus3ZFiUsPZ/5ddV1cxm3zdgAEpjWnsYdr4ac8g9YYYSl8ZlFYEpJVhaZNQtXEIf4owtK1LhKyvvf7+p/Gayisr1xcpbmDjM0Vw2tjTNAtKP3if6SI9TEs9UeP40KHeb7oIpB0ECBAgQIAAAQIECBAojoAAtDhtoSYECBDIX6Bv39TrmCNT/XHfT/XfOir1uv5P7X6nZv3NjeHptdentNqqqe7A/17qOtY1vtczxWfNxhH1bSytob7+371IG3uTNglKK3qbNumNGr1OZ73bxjt8cNrsxl6tL76UfZoHpc23mxQc7zetHJr/QUD6fg/TD4btN4allZNEhYH3mzZRtEGAAAECBAgQIECAAIGqCAhAq8KqUAIECBRHoO6bh6V0wW9Tw/ibU/13v596//iHba5cw+NPpPoDPp+d3+unp6a6DyZVanMBOZ1Y16vX+z0zo3dm49Lm4HT+/PeH6TcOy28+HL+yh2nDB8ez4fYRnMZET+1ZSu83bXzVamVQWrm+SHHxftMPQtPs/aaVvU0rhuU3743q/aaLSNpBgAABAgQIECBAgACBxQoIQBfL4yABAgS6vkAMVe997R/Swq0/mhpO/Wla+MaU1OvsM9KSgrT68Tel+gO/mNKMt1LdMUekXvvu0+Uw6hp7wKbRo9//NNa+zcFp5ftNF9PDNOuJWjo+ozFkXbCw7UYxpP/Nxneoxqc97zft1/hMEZxGYNr4XZ4YqlkP06z3aSlgbQxUaxVetx3EmQQIECBAgAABAgQIEKiOgAC0Oq5KJUCAQKEE/j979wHnVJX2cfzJDL0KgmJBAVFsKIiKa3ddK4pl7b1iXSur7lpQsbtWLGvv2HWtWHhtrIIKNhYUxY4gRZoKUmbynv/J3HAnk0wymQwp8zufT0hy67nfO0x58pznRNZb18r/7xWr2Ht/i973oFWMeNUPjY/8dW+LrLlmvK/RRYssOvINi956u0VHvOaXR851Q+evGBrfpjG8yLq+qSZ5qprwKZxhGpsYymWWhmufBlmnqolaa6pogvhil9WaTX3TNq1dbVMFTUND8asCpOF6p9UmhqK+aQI+bxFAAAEEEEAAAQQQQKAYBQiAFuNdo88IIIBAFgKRTftZ+YfvWuXg8yz66ONWefa5Znp0dsPKu6wcq5c55Sc3S7sLsKn16G4a9l62z16x9/ybViDiAoaaFMl69Mg82zRc39QFSOPD8V2ANKqAabVJotz7IHD6+4K0/am2geqh6uFqnCbGWxPfx/dTyqzqtVZlnIZrmMYyTF1ANQiqBtmmem7XjvqmcUReIIAAAggggAACCCCAQL4FCIDm+w5wfgQQQGA5CkTccPDyRx6w6DlnW+Xwxyz6wktW+fkkK/PDsM0qXOCqycABFnFBz8gB+5kfQr4c+9cYT1Xv+qZVAdLwcHwfOK0KlIYnjPIZqMogzbQpMhqvb/pNtcBpyqCpjl3uarYGdUw1/L5quH68nmnVJFGJ2aiRVq0y7RnbIYAAAggggAACCCCAAAIZCxAAzZiKDRFAAIHSEYhsvJGVu4ddfYXtM2CA/ffll22Ru7yPxr1nPXv2LJ0LLeErybq+6QKXOTrL1R1Nk2Eaz0RVgFVD++tS37SiMlTftPoI/1oDp6pvqomuqgKk4eH4fph+sDwUQLUOHahvWsJf51waAggggAACCCCAAAK5ECAAmgtFjoEAAggUscBSN8O6puGhNQ4Bn2W5xhpm7pHppFCSiSoImqSGaSzD1E0AVTVcP177VLVQs6lvOnWamR46p/839k/4dWhx7KXqm4brmYYCpPFh+259fMIorVfgtLy8xqFYgAACCCCAAAIIIIAAAqUnQAC09O4pV4QAAggggEDOBeL1TdeyjAOnUdU3rQqa6jk8HD8eKA0HVYN6p9nWN/3+h2pBUyGkDJwq+qt6rfEapqF6psHw/XBQVcP4tZz6pjn/2uKACCCAAAIIIIAAAgg0tAAB0IYW5vgIIIAAAgg0UgFf37RzZzfRlnu4lmnGaXTx4nhGqQ+cBhM/uQBpPHDqMkzDdU9NGaeL3H6ZNkVG57isVj2+zrK+aTir1AVI49mm8aCqmxAqGLZPfdNM7wzbIYAAAggggAACCCCQcwECoDkn5YAIIIAAAgggUB+BSLNmZm7CLv9wB8o4cPrbb662qQuEhgOlSTJMw5moNscN31fN0kxbtvVNm7trUkZpVSZpjYmhwsuDDFT37C0y7RvbIYAAAggggAACdRTQr04TJ8Z+hWre3KxbN7N11nG/f2X6C1gdz8fmCORLgABovuQ5LwIIIIAAAgjkVCDSpo2ZHnWobxqNulRQ1SpVoDQcOI1nmLq/CoKgalUw1W87f34t4+uTXJayU+tT3zRdhmlVANUHWN3QfuqbJrkHLEIAAQQQQAABL7Bkidm995o9/LDZe++ZqWpRuK2+utnee5uddZZZ9+7hNbxGoHgFCIAW772j5wgggAACCCBQT4GI0htUC1SPtdbKPNu0oiIeGFWANDwcPxoOlIaDqlq+YGHdevzb72Z6ZFPfNF7D1NUujb9eNixfwdL4sH2tb9vWZXuQ7lG3G8TWCCCAAAIIFJfAO++YHXec2Vdfxfq92mpm/fqZdeli9uuvZt98Y/bhh2a33GJ2552xIOhll5kxd2Rx3Wd6W1OAAGhNE5YggAACCCCAAAK1CvgMy2zrm86a5YKnbui9C4iGh+OHA6fRUN1TP0w/2/qm7irCE0GFX9e4wCblsYmegrqloazSSFUAtVogtWp9hPqmNShZgAACCCCAQCEKKOvzxBPNlAG6885mF19stsUWNYe7z5xpdvvtZtdea3bVVWZjx5o984z/rDQvl/Xll1/aIYccUuPc+uC2uRu33759e9tggw3spJNOchmrpKzWgMrBggr34f8PP/xQ1L4EQHPwhcAhEEAAAQQQQACBTAR8Tc9VVzXTw7VM8y2jqm9alVkaDpRWG7avgGoQONVzXeubLnVZrTPcXzx6uBYOloZf+5Xhf1TftFMnP+FTtUBpQoZppCqw6ieGor5pWJDXCCCAAAIINLjACy+YHX+8WRMXBbr7brNjj019Sn3Ge9FFZkccYbbXXmYjR5odfLDZ88+blZWl3q+h1ixYsMDGjRtX6+Fffvllu+GGG+ywww6zG2+80QdFa92BlRkLjHUR8GPdF8xBBx1k//jHPzLer9A2LLkA6KJFi+ypp55yn1CMdb/3z7G1117b+vTpY7vuuqtL2XaZDXVob775pn322Wcp9+jkftk/9NBDa6z/4osv7Mknn7Tvv//eWrdubb1797Y///nP1qNHjxrbsgABBBBAAAEEEEgnEK9vuuaamQdNVd90rpvlPl7DNFTPtGpZtaCqG67vt82mvulPU83cIzFQmvi+2nW2dfVaqyZ8qhY4dYFS/z7IMNVzkIHawa3Lx19e1TrOGwQQQAABBIpLYNo0c7EL9+Gm+8E8fLjZX/+aWf+7dTPTkHllib70Uiwj9NxzM9u3obZ6//33fdanjq9a7r///rv9/PPP9uqrr7q6pvfa/fff7zJcl7j6pg83VBca3XEfeOABHxtTALSYW0kFQOe6X/JPPvlk+/HHH/096eh+qX7llVf84z1X2XfIkCHWTDPLZtiee+65Wj9lUEAzMQCq4OtNN93kz9DGTcSwePFi++ijj+yJJ55wqeNX2SabbJLh2dkMAQQQQAABBBDIXsDX83QBQ9Mj2/qm4azScD3TeFDV1TUNsk7rWt/0V5fVqke29U19hqm7NgVH/WsFSt0jSVA10q5d9pDsiQACCCCAQJELXHxxrL7nmWdmHvwMLtmNLrdnnzXbaCOzK66IZY5q4Ee+2kauIy1atKhx+r+6qO7BLk1VyW+PPPKIy1zdy/bff/8a27Gg8QqUVAB06NChPvjZv39/u/DCC33K808//WTnn3+++9TiHbv55ptt8ODBGd/tr6qqAp922mnxTxjCO7d1kwWE2/jx4/05FGRVsHWbbbaxpUuX2n/+85/4uYe7j1u6qLowDQEEEEAAAQQQKECBrOubulE4PqO0KpO0xsRQ4eVBBqqeF7tCZJk2pZTOcVmtekz+ulrGaa3ZpkF906oAaUTPVVml4WzTaoFUtz7SsmWmPWO7IhOIulpmbpJja+VyqjstWVpkvae7CCCAQOYC8+aZ3XdfbL5HFybJqq27bmz4/G23xY71979ndZgG32mHHXawSy65xA/TPtEVOx0wYIC1SlKrXMlzn3zyiZsI6is/UlejhlfU7wUJ7Rf3QbCyS1dzM0Wt4CbMnOcwR40aZQsXLrTtttvOVlpppfgeGo08ZswYf0yNRFY8qCzFqBVlqGq08cSJE11JgiYuuLyR9erVy7+OHzDJC/Xnrbfe8lmv66yzjin2lTiBZXBMrf/uu+98f7faaqsaI5L/+OMPU23VSZMm+etay31Yvq670ausskr8zKr7qRHOs/X7mmvTp0+3CRMmuMpHnWzllVeOb1csL0omAKqb/MEHH1hL94vqZW6KsuATAX2hXn/99bbvvvvaiBEj7IQTTnCTnFYPXCa7WTNmzLD5bgiY/hNk+qmB0oKVgq2aE9tuu60/bNOmTf3+U6dO9UPzFQzVf0QaAggggAACCCBQSgIRNwmBr22abX3TNBmm4Qmj/ND+isrM+bKtb9rCXZP+IEqXYRrKPFUGasT9/kcrPIGo+4Mz+vBwiz79rEVHvmnfmru/al99b0s7rGyR3XaxyEEHWNnAPWLL+RcBBBAoAQEXBvGTHu29d2xQSLaXdMwxZgqAuoGyVqgBUF3b8a7QqRLSFLT7/PPP3Qz3/eKXrIDeFS6N9dJLL/XJasEKlUtU4pwS6RSQDJqG0yuJ7q677vLJdpdffrnpGEE79dRTbdiwYX7UseJGv6lme1XbfPPN7cUXX7TOKqgaaopLnXHGGT74GFrsg4+KKWm/xPbpp5/akUce6YOmijkFTSOMNex/4403DhZZ3759fdKd6qCqT0F/r7vuOjvrrLP8dvfcc49/rZhXuClgq1HV11xzjY+tKeC64YYbxjfRaGc9zj77bPvXv/4VX14sL5bd2WLpcYp+Kgqupih8EPz0C9w/GgqvL6LRo0f7IOgBBxwQrEr5HGR/KgqfSVNRXgVg1XbZZZcau2iZhsfrP8Bxxx1X7T9VjY1ZgAACCCCAAAIINBKBetU3db+Yawh+jWzTYFh+OKiqbX/9tfrsTumM/3BZrdnWN1XgNKGeabVAqlsfnxhKAVSXWUJ903Q3JPv1lS++bJVnuJFgX38TO4gSItZdxyKuRERU0x275dFHn/CPys03tfLbbrZIv02yPyF7IoAAAstJwFX7c5l5qU/24IOxdTvtlHqbTNYojqiqOi6s4mIb5uZYSb2XZph306HkpSmJTYFBZWMqUS4cAFUs6Bk3nb0yNxXEW2+99XzGpkYLKyiq7EbFbRKbgn2TJ092tVP/altvvXW81OItt9zi643e51Jsd999dx8L0kzpt7lIseJDCqj++9//jh9OcStlpSqIeeCBB9oee+xhlZWVftTws67OwJZbbumP/Ze//CW+j+aW2W233WyaK+SqgObAgQPdrzO/2oPuxuoadV5NEBUeaaz1CgQrNqbsVq3XMdR0nQoQK4tTCYK6npnu56BiVRo5rWta09WcV+BXyYMKpD799NM+k1R938l9IRVraceSCYDqC1VNKcDJWhAAVZpxNgFQDWVXdFzB1GRNnyzoi7hr164u+SE2s2t4O6US64tHKdP6D8GESGEdXiOAAAIIIIAAApkLVKtv6naLZLhr1P0+5yd6qgqQhrNKo1XB1KQTQ2Vb3/S776sN01c3l+VtJHS6zF2FC4Iqg9QHSkNZpbVOEkV90wTImm8rL73cKi8e6vEju+1skbNOt8h221bL1I26STSiI161yiuvMftgrFVsvYOV3XunlR18YM0DsgQBBBAoIAEXz3ITAKXv0BprpN8m3RY6hktGdIG42rdUNcGePWvfpiHXBsFABUCDpvlhFPzUOg1/Dw/hVnblpptu6gN9r732mu2sCG6oaZj4tddeGy+p+Le//c0ntimT8o477rBz3cxQmvMlaAoQKlipbM+gKSh59NFH+7hROBtT64844gg/dP/iiy/2GZgqr9hcI2tcU4BTwU8FLbU+aIMGDfITbisWpT5ofdA0IXg3N4PV119/7UtDari7gqF6VoBTTUFa9TFoZ7oCscpovfXWW/0EUgqAaoT16aef7oO/GvqvofR6X6ytZAKgqvWpproMyVqwPJggKdk24WVBBqgCn/pC+Pjjj33qsIKYm222makuaLhGRLrz69jqg77o1YdUAVAFWVVXIVnTvmpKYVbNCBoCCCCQTkAfzKT7fqFPHYOmbdNtH2zLMwIIIFCUAvGJoXpk3n3VN50VyzaNBVBdLayqmqZ+Eqh4TdM5VQFWt94FWSN1+X2t0oVGFZjVY3Lm9U2jSsEJgqUKnrqs02Xvw6+1LrS+BOub6vf2oMV//l17vUWGuOCnm5y07KF7rWzvgcEm1Z4jLlUpst++FvnrPha9cZhVDj7PKg49yirkNCCWNVNtB94ggEDRC4R/By76i8ngAnJRnSUXx8igq/XeJIj/fPNNVda/O+KVV17pj6sgYjj4qYWK7RxyyCF+eLxKKCYGQFUXM3E+GZU9VABUH8pq+Hy4bbHFFv7tlClT/FB7DatXAFY1OZUclyyI+M9//tM0BF6xqBdeeMH2228/nzynIK76qyBruGnYvoajP/744y7Y3DO8yr8+77zzfPBTb4JR0r+7D/tUI1U1UMPBz2DnffbZxwdAg5qfwfJSeS6ZAKhupFrwhZ54g9pVfToebJe4PvG90pvVHnroIZfaXe6/SPVLlf4DvfHGGzZ27Fj/xRZ8oQXHTXV+HSuTPug/hdKkk7UO+oXdNQVCZ82alWwTliGAAAI1BNJ9v1i8eHF8H31amG77+Ma8QAABBBqTQPNmZqusHHtkeN0RVwss4v7IKJs91yLu+2uZex1xEzjFnt17Ldd6ty6+fO48i4Tqe6U7VUS1yGa4Idx61KFFXWZJZYcVLOp+v/TP7oP6Shc8ja7Q3r3XsmWvo255pVseVaJBkfz1q8koKt94y9pfcLG5FBYrf/t1i2zSN62Q/pCNnHma2WqrWsWBh1n0sKNtzpuvWOXqq6fdlw0QQKC4BJQNF9RHLK6eV++tG0nt6j5WXxZ+9/LL5mpHmrlpSerdgmO40eO1/jioCl3U+3zZHkCBR7UgE1Svg2xQxXeCEopaHrRg8h9N+pPYNEFQYtNER2raL3GemSDAqiC7kksUAFWmppqGt6sPiU3zx2gSp2+//Ta+rTJV1ZSEp2zMxKbh6Hokawq0JjYFek866aRqi/X3nyZDUtaphsGrhT9QrLZxkb8piQCovqj0zUst8QsvuD9t3Ke+avplKF1TMFOTFqmpdqdqQwRfbFquiLn+86h47p133um/mFUDVC3V+bUu6EPQVy2jIYAAAggggAACCJSmQNT9/qlHnYJnLvgZmTc/FjD1QVMXENWzMkpdcNQHS33QtCqo6gKqPngamnghE82I+524/OfpZnrUoVW6TMmoC5zGgqXuOQiiuuCoXx6817OCqgqktm9nbircOpwlB5s6x9aXXOaDyWV33ZZR8DN81rID9rPouI8ses311vqq6+zXW24Ir+Y1AgggUDACblR1rc2Vc3RBL3M1HM1cgl/WzY2m9kHUDTYwNwFO1odZLjtq6LdaMPJWGY9BkodqY9bWNGJXcaNgCLq21XDyxBbMvt6+ffvEVdVmfw+2CwKgyY4VHKB79+7+ZTAiOQiArpFF/YLg2oNjB88K+qump7JXVSJSs9wHLTwBVLCslJ5LIgCqmaoUoFy4cGHKAGcQ+GzWzH16n6bpWMOHD/f/QVQwNviC1W6q76misYceeqhPTVZhWxWqbV1V4TecSZV4mqAP4f9Iidus7j5dThXBV99feukl90lL03gKc+L+vEcAgeITULHp4EOUXPVe34v0A1SfTAZDHlIdW99Dg6bvT+m2D7blGQEEEECggQSU5dHFZZuGmmqH6rGsaElopV66kUoKhEbccHw9m7JKFTQNP4fWx5dXJREkHC3l2zKNunKP8imx8lMpNwytiLqsSjcOz5RJqiBp1A3FV+Zp7LV79pmnVc+h9dY2lsAQOlStL5UUEfwu3vztUdb0f6722xabW9mhB9e6X6qVZRf+0yrue8iaP/MfW3rxBRZdfbVUm7IcAQSKUEBZeOG/9YvwEjLq8p57mqsrGZu46Oqra8/crO2Ajz4aW7vXXrVtlf91igtp3hW1IAiosihBU63OTOJCwfZ6Tvb3UfiY4W0TXwfbKY6jVlu5MfVdLdg2yFCubR+/Q5J/ksWd1BfVG1W8S03xJw2F1yzyqoGqUcuKb5VqK4kAqG6OZrBSpD6ok5l4w4LlQaAycX34vYIBmsxIj2RN6cy9e/f2w+A1JF5fIDq/mmp4pmqZ9EHHSvUF94srzq8AqK4hGA6f6lwsRwCB4hFQPZmg7EYue63i3Kolk+77RfgXAH2CmW77XPaRYyGAAAII5FCgc+c6HyyqAKgmgHI1TaPxWqauhqnehyaGCk8Y5eugLllWbzPdSf2Qfg3/d486tabuT5WqSaH0HAlqnbrnahND6b1bv9T94TZLda1btrAWr77uT1V2Yu2ZPrX1J+KydyNHHGJ23U3W7u13rOxUF0GgIYBAyQgoQBROBCiZC0u4kNXcZzeaAFxD4TUhufsToc5NPx6uu87c6FczN19QQTfN2K7AoYZ7//nPf/Z91d83itkoC1T1PfvWVjOgga4uGDKvOqCpmmZ8V9Ms9WpBycVgSL9fGPpHo4sffvhh0xB9DZ9P1zTTvIKf+pvv+eefN9UxDTdNEqVWqvVxG00ANAhM5uoP++ALUkFJtSAAGgQ5/cKEf3Ldh4TD8xYBBBBAAAEEEEAAgToJRNyssKa/jt3D5Wlm3KKanLMqQOoDpcGkUOFAqlsf1aRO2k7rFQDVZE+ZNgVZp8+IPdw+4T3Dr4PDqf8KAUdbNHcpNrEAbWT3XYPVWT1r/6gLgEbffNuMAGhWhuyEAAL5F9D8P266EfvHP8y22cbMDXTNuOlzpcMOi30LP/FEs3XWyXjX5b6hgovBbOya0TycALfhhhv62p8a/p0sAHr33Xe7IO91tvnmm/vJiHLdeZ1fTYHHG264IV5mMTjPvHnz4rPGB4HJ9ddf369+7733/MRFiXPOaCShhvQre/PDDz8MDpXy+e233c8y1/bYY48awU8t1+Tfaok1QIMPCoKMVL9REf5TMgHQICCpjMxgxq3w/Qhm/1pvvfXCi5O+njRpko0ZM8ZatWqVdGYs7TRjhvtlzDWlDKsF51cWqtKTg5Rlv9L9oy9mzaSlL5wg8h+s4xkBBBq3wHHHHRevSZNKQrWJb7/9dr9a33cOOuigVJv65dq+f//+tW7DSgQQQAABBLIViLRtq+L3KoyWceA0qr+iFQStyjQNZ5WGs03NBU2rBVXnu2BrHVrkj6qa/5rZPYus2PCpIj26+7fR72PDKcPreI0AAggUi8BGG5mbw8TMTQxuu+9u9p//mAv0pe+9RmSrxuiIEWaKxWkIfaE1xWY05F2TVWueFpUWW8dFaU877bRqXR0yZIgPgCpDVFmgQZBRGynz8qyzzvIjig844IBq++XqzV6udoACrwoyagTgzTffHJ8MSTGkM844w8eMNnBFVjVRklq/fv38vDSvvvqqu3fn2W233RbPWtbfexdccIHf7kDNhJVBCybmHuUKwirIGa75+bJLEb666gYnzlsTzIkTZKhmcKqC3KRkAqA77rij6Yti5MiRdsghbqhKqCl9V/8Z1FTTM13TLFiK/qseiKL/a6pqcKgpkDlhwgS/RF+caqoNqlm2NGPY+++/b1tvvbVfHvzz5ptv+jRsRfAVWKUhgAACgcC5554bvEz5rB/sQQBUtWyuvfbalNtqxfTp00t26EKtF85KBBBAAIGCFYio5rSGtOvR0zIPnLo/0oJsUz2Hs0rjw/bd8spZv1jFjJl+qH3ZtJ8t0qZ1/S2q6vy74qL1PxZHQAABBPIooD85NMJaORXbbWf297/HHvosK1lTxqi2+d//zMVEzF54wVyNyGRbLt9l4YmnVdMyMStRMRfFhTrqZ02obb/99naiS2H9t6sDoOHi++23n23kIsMfffSRvfbaa/abm0xQCSTnnHNOaK/cvVQynAKYCr7qWUl3u+66q/+bTcFHTUikmNL//d//VUuoU7boVlttZXfccYcPnmqibgUiNVxdfd7GpfQqeJpJU6BUf0cqYKxr3Xvvvf1kT8oM1XkVL1M/FFxVEl8wwZOG2Kvde++9PtN04MCBfm6cTM5ZSNuUTABUWZ/d3CfQmi1rhPt4YjcVuahqjzzyiPtd6RcfyEzMiHr33Xf9JwSqrRDMuKWbrtRizRR2//332/nnnx+PjCsSrqi4itPqi1BfoEE7+OCDTZ8q3Hfffb6IbPAfU4GLR6sqBqvALA0BBBBAAAEEEEAAAQQyE4io6Jyrwe8fbpdIit0qXQbNHFffTa3T+n3NZs6yqKsDF3ETnWTdfp4e2zXhD+msj8eOCCCAQB4FXNzNevWKZYIOHRqr6+nicbbZZrFvsapu4gbVurlHYs/qqspoPvaYKzHSOY8dD506cXi2yhyu5sq4KEnkSFegVJmWmuAqWVNCiQKGSkB54okn/EPbKcPx9NNP9/Gc8LD5ZMeozzLFrcaPH28nnHCCDzgq+KqmEX6HH364jzVpzplw0yjm/7ko9KBBg3zSnybiVlP2pvp8ySWXxONV4f2SvdYwfJUA0FwROndwfo1oVmD0lFNO8QFaBUNVL/Soo47yh9Hz66+/bs8995x9+umn1sbVyNbk4MXWXE3y0HRYxdb7hP6q/sFFF13kPwFQRF9DzfXFNXr0aB9Bv/HGG32EP7yb0punTZvm6yZoNqygjR071qdAi0f/oRSl138inUMFaBUs1RdI+ItTnzycdNJJ9vnnn/uMUPVB/zn16YMCsAqYKiU7qJ8QnCvTZx1DEyTpOsIB3kz3ZzsEECheAX2QEny/0XCNoH5LqisKMkCVyd6lS5dUm/nle7qpIV988UX/Wh8iBcW2a92JlQgggAACCBSYgIYQaoILtQ77H2JN3h1t5e+Pssjm7i/7LFvlPfdZ5XEnWeRvJ1v5zddneRR2QwCBQhS4+OKLTcEkZd81tqZJ0l3czJ580tyw7+RXr6Com0/VZQkmX1/sSxVfUflDxXsU30k203tDXuOiRYt87EjBxEz//tI+EydO9DGlXi6SnW2fFbtSFunUqVP9BEqrrLJKRpe62I2GUPxM24cn0s1o5wLYqGQyQGWpoIDSgxVk1JBzPdSUGXrmmWfWCH76lSn+URFZfTpw0003+S/Kxx9/3G+pTwaUcqz6EIlD2RUgHTZsmO+DUqiVeaqm5UqvVpQ/2+CnPxD/IIAAAggggAACCCCAQFqBpbvu7AOglU89Y+X1CIBGn3zGnyuypyuaR0MAAQRKRGCNNczuucfcUGxziRXmEsfMXCVANxxa8RNzQ8SVlVgiF5viMjRLvBLM8tWaO+xMSjSG+6d9kk3gFN4mk9eKUSljVo+6NAU9E0tE1mX/fG9bUgFQYeqL4Un3MYai+ZqQSKm8yn5KFXhU2nOqpvqed955p699oGNpSHvXrl1THkvH0RekitOqqO3XX39tyiDVPg2ZRp2q/yxHAAEEEEAAAQQQQKAxCizde6DZZVda9LY7LHr2GRZJGFKYiUl09BiLvvq6WdfVLbK9K5hHQwABBEpMQAFPDYHXg4ZAqQuUXAA0uGGK5uuRi6bCr0Hx10yPp3oMSkmmIYAAAggggAACCCCAwPIViK62qkVOOdGi199slccMsrIXnjU/CVOG3YjOn28Vx57gty679CKLNG2a4Z5shgACCCCAAAKFKFBWiJ2iTwgggAACCCCAAAIIIIBAfQTKhlxgtv66Fn35VVfH0wVDXY3QTFrUjQOtGPhXs88nWWTArhY54rBMdmMbBBBAAAEEEChgAQKgBXxz6BoCCCCAAAIIIIAAAghkJxBp187Kn3/abJUuFr3vQavY7i8WHRebcTfVEStffsUqNtvKFcUb5WprbWxlwx+sU+ZoquOyHAEEEEAAAQTyK1CyQ+Dzy8rZEUAAAQQQQAABBBBAIN8CkbXWsvIP37WKvfc3G/2+C266CS+23cbK9hxg1msdi3RYwaIz3czxn423ymefM/vkM9/lyD4Dreyh+yzSunW+L4HzI4AAAggggEAOBAiA5gCRQyCAAAIIIIAAAggggEBhCkRWW83Kx4yy6D33WeXQK312Z6UyPJO13htY2WWXWNnAPZKtZRkCCCCAAAIIFKkAQ+CL9MbRbQQQQAABBBBAAAEEEMhMIFJebmWDjrPyHyZb+ei3LeLqgz5kFfa0ezy7Qlsru/ZKK5/wsTX5bBzBz8xI2QoBBBBAAIGiEiADtKhuF51FAAEEEEAAAQQQQACBbAUikYjZFv0tsvlmdswlF/rDrL/qSrb/4DOzPST7IYAAAggggEARCJABWgQ3iS4igAACCCCAAAIIIIAAAggggAACCCCAQHYCBECzc2MvBBBAAAEEEEAAAQQQQAABBBBAAAEEECgCAQKgRXCT6CICCCCAAAIIIIAAAggggAACCCCAAAIIZCdAADQ7N/ZCAAEEEEAAAQQQQAABBBBAAAEEEEAAgSIQIABaBDeJLiKAAAIIIIAAAggggAACCCCAAAIIIIBAdgIEQLNzYy8EEEAAAQQQQAABBBBAAAEEEEAAAQQQKAIBAqBFcJPoIgIIIIAAAggggAACCCCAAAIIIIAAAghkJ0AANDs39kIAAQQQQAABBBBAAAEEEEAAAQQQQACBIhAgAFoEN4kuIoAAAggggAACCCCAAAIIIIAAAggggEB2AgRAs3NjLwQQQAABBBBAAAEEEEAAAQQQQAABBBAoAgECoEVwk+giAggggAACCCCAAAIIIIAAAggggAACCGQnQAA0Ozf2QgABBBBAAAEEEEAAAQQQQAABBBBAAIEiECAAWgQ3iS4igAACCCCAAAIIIIAAAggggAACCCCAQHYCBECzc2MvBBBAAAEEEEAAAQQQQAABBBBAAAEEECgCAQKgRXCT6CICCCCAAAIIIIAAAggggAACCCCAAAIIZCdAADQ7N/ZCAAEEEEAAAQQQQAABBBBAAAEEEEAAgSIQaFIEfaSLCCCAAAIIIIAAAggggAACCCCAAAI5FoguXmz2zbdmc+aYNW9utuYaFllxxRyfhcMhkH8BAqD5vwf0AAEEEEAAAQQQQAABBBBAAAEEEFguAtFo1KLPPmfRh4db9NXXzRYsrH7ejXtb2T57WeSUEy3SqVP1dbxDoEgFCIAW6Y2j2wgggAACCCCAAAL4lkqVAABAAElEQVQIIIAAAggggEBdBKKffmYVx59k9uG42G5t25htuYVFVl7J7LffLfr1N2afjrdK97Drb7Kyi863srPPqMsp2BaBghQgAFqQt4VOIYAAAqkEylOtYDkCCCCAAAJFI7Bw4UL77LPPctrfpUuX2ty5c61Xr17WokWLnB6bgyGAAAKlIFDpsj4rDz/a7PcFZv36WtmQCyyy818soqHvoRad9KVV3nK7Re+4yyoHn2fRDz60sgfusUievrd+//339te//jXUw+ovmzRpYu3atbPVVlvNdthhBzv88MMtEolU36iA3z344IN2880327777mv//Oc/M+7ppptu6rd96623rE0bF8h2bfvtt7fffvvNXnzxRevSpYtfxj8xAQKgfCUggAACBSwwcqTZM8+YvfJKR9fLJe7RxP773wW2xRZme+xhduCBZmuvXcAXQNcQQAABBBBIIvDtt9+6n2Xuh1kDtBdeeMG22mqrBjgyh0QAAQSKVyD69jtWeeChZhUVVnblUIucOzhlkDDSax0rH3aDRQcdaxV77WfRJ562ShdkLH/kgbwA/PHHHzZuXFXGapoe3H///fbSSy/ZAw88UDQfhv3888/++vr165fm6qqvDkz0AWDQPv74Y5s/f74tVm1XWjUBAqDVOHiDAAIIFIbA6NFmZ55p9v77QX+auE/1orbCCmbTp7f0y7Xu4ovNjnYf4l5xhVnnzsG2PCOAAAIIIIAAAggggAACMYHo7NlWsd/BLp9iqZXddZuVHXdMRjSR3hta+ei3raL/NhYd/rhVbvUnKzv5xIz2baiNvvvuO+vQoUO1wyvYN2XKFHv22Wft2muvtSeeeMJnRN5zzz3VtmsMb0444QRTwLht27aN4XLrdI0EQOvExcYIIIBAwwvceqvZGa7Mjj7I22QTs1NPNRswwGyllWLDOKLRiE2YYPb00+aGSpjdfbfZa6+ZPfecWZ8+Dd8/zoAAAggggEB9BVZwn+gdeeSRaQ/z5Zdf2mh9KuiahvptsMEGKfeprKz0f/StyOzFKY1YgQACjVOg8rIrzWb9YpFjj8o4+BlIRVZe2cqfedwqNtvSKoe4zNHDDrGIG26er6ah7noktk5usqY+7o+hVVZZxU466SQ/BFyTPRXTUPjEa8rm/TXXXJPNbo1iHwKgjeI2c5EIIFAsAsOGmZ12mpnK8Nx2m9lxx5n7oV2993q/4Yaxx+mnxwKkjzyiei9mY8aYrbtu9e15hwACCCCAQKEJrLrqqqZhiunaXXfdFQ+AHnTQQXb22Wen3GXJkiU2a9aslOtZgQACCDRGgejvbmKj2+80a93Kyi6/JCuCyCZ9LXLEYRa9/yH/iJx2SlbHWR47qY6mAqAzZszwtaY33njjGqdVvehPPvnEvvrqK+vRo4cPnCb78OyXX34xDU9XULVjx44+y/S9997zH7b17dvXevfuXePYM2fO9OfW8ZLV4Pzhhx/s119/tdVXX93at29fY38tUM1TffjXrFkz23zzzf22STdMsvCLL75wVQ4qbJ111rGmTZvW2ELXpJqhv7uvC23Tv3//lEFiZZLqg8hJkybZvHnzbK211nJ/a67rPRIP/Pnnn1tZWZmvw63As/Z53w1ZlJuG9uvnfr5bWb47wPkRQAABBGIC77wTG/au2uKvv252/PE1g5+JVhoS//DDZuecY+6HktnAgWYLXE1zGgIIIIAAAggggAACCCAQfdX9YfHHIovstaeb6X3lrEHKXD1Qteh/ns/6GMtjx8mTJ8dP06pVq/hrvVBgcOjQoa50WGc/WdKgQYPsL3/5i63sXIYMGeJG4C2rpant9UHdhi7z5GH3B9epblhe165d3RwMB/oRDBtttJGbk2EPW7RokTaNt9tcFov2ueqqq+LLwi90HK1//vmajhrJMHjwYOvWrZsdfPDBfuInnfOQQw7xAcvwcVK9VkBTx582bVq1TT799FMf6NW177fffv4a/vSnP/nRFVqX2FQ+QC4KIB9wwAHub9Pj7c9//rMPxv7tb38zTWYYbgrUagKmn376yWSz3nrr2VFHHeX+Ph3o9zn//PO9f3if5f2aDNDlLc75EEAAgSQC7kMyUzan+5lsLtnFttkmyUa1LNLPVw2Ld/W+7frrY8HTWjZnFQIIIIAAAggggAACCJSAQHTCRLM5c1JeSfTRJ/y6yC47pdwmoxX9Nzc39tyio961SjehUqS8PPVum/bLy4zxmvzn0ksv9f1ShqUyFsNNgbxn3AyzK620kh9RoCCdskA1A7v2m+D+oHrqqafCu/jXV7gJF6ZPn+7+Xjvd9t57b58Vevnll/vJlk455RRXkszVJMtB08RNGs1w7LHH2m677eaDmNddd509+uij9s033/is0GyG9CujNDje/vvv74OSykLV7PNj3BDC3Xff3U/CFGSsykIBYZUVUE3Rrbfe2pTZqpnl33FZO7fccoutueaaPlgbvmzNPq9JCJUBqmMo2PrGG2/Yk08+6easuMK6d+/uRji6IY55agRA8wTPaRFAAIGwgJuw1g3DMD+7uyY1qmvTsHjVA1XmqKv7bYcfbm7IRF2PwvYIIIAAAggggAACCCBQTAKVZ59rPsszTacj3dZMs0XtqyNueLOt2dVs/ASr3H7nWjcu/+p/Zj171rpNtiuVjdm6detqu2u4uwKEI0eOtG+//dYPxb799tv9c7DhK6+84oOfCvJp+LuyG4OmmtSqM/20m2ThNTe5ws47V78+BT9vvPFGHwAN9hngJmnYxE3YoEzJ7bbbzv395f4Aq2dT8POGG25w80G4CSGqmsq/6DwaTq7grAKYdW0KcCojVEHNiy++OL67MmA1jF/D1++44w6/XsPeFeBUUzZr+Hxnull6lcF6q5u0QvdB2arhpgCohrprJvog+/bEE0+M7yOrfAZA3VcwDQEEEEAg3wKa0EhNM79n21z5Gj8E3n3waW+/TfQzW0f2QwABBBBAAAEEEECg5ARatqz/JeXiGPXsxWluwgRlSIYf//jHP9wourt88FNDtkeNGuWHp4dPdeWVbiIo1xQADAc/tUz1OjXMXO16DadLaMokVaZnuGmWdZ1XLdlw9vC2mb5WTU4NLw83ZWGeo3pnrimoW9emmqMTJ07013zuuedW273cZfHedNNN3rJnVcBatUEvueQSn7EZDn4GO+6zzz7+5ezZs4NF1Z411D0IfgYrlDWrpuB0PhsZoPnU59wIIIBAlYBmcdcoEjfSoV5tzz3NfTJobmhCc9tppz/qdSx2RgABBBBAAAEEEEAAgcIWiOy2i1nX1VN2MvrOKLMvJ1t02s/mBo3Vr02tqit55GEWaVpzgp34wVNM7hNfX48XChC2cJMmaJi1hry/+uqrftIgBTVfcMPqNttss6RHVxBQTUE/TQKU2DTRkZomEUpsqhPapEnN8JmGhqsp4zEXTTU21b/Etu222/pFytSsa1O2q5pcWiYJYO+0007u78Zl5REUDNYkUuE2x5VY0GRI48eP98PgtS6xXmqwvSZJSmxrrLGGX7R48eLEVcv1fc07uFxPz8kQQAABBDRpkZtc0BWHNnMfJNaruQ8Nffvuu3K73xXt1vCFZD9EwydRPZegbeOKjyabLTBYr2d9CvnRRx+FF/EaAQQQQAABBBBAAAEE8iBQdvqptZ618uZbrfL0sy363mg3XGyPWretbWX0xx/NpvxktvZa1uT+3NS8rO18qdYpO7FDhw7x1QrEafj5Y4895gN5qlGpSXjCTbO+z5o1yy/SZD61tR/ddWpio+bNm8c3U73LZE0TFKlp+L2Gf7dp0ybZZhkvS3cezUivDM3EEgC1nSAIgAZByNq2DdZpsiiVA9CQ9c8++8zXPA3WJQsEB+v0HASSw8s0m72agtb5bARA86nPuRFAAAEnEEygpxnd69uC3wXGjm3qClXvbFOnalbCHxIe1WcqDJ9TP1TTtcSZDtNtz3oEEEAAAQQQQAABBBDIj0Bk4AA326oLgD71rEWvGGq+lmcWXYk+EavZFalHEDWL06bdRQE51aNUjcu3337bT/Dz4Ycf+gl4gp3DgTfNzh4E5IL12T4vUCaLa8pITZZdmey4tf0tpVngk7Ugc1JB2UzPExxHwUw11RfNpMnqiCOOsOHDh/vNNZmUhsKrtIDqpLZzE2FtueWWKQ9VplqxBdoIgBbojaFbCCDQeASU9alJjFzt7nq34Bjz5pW5T+v6uePpkdgWufP9zz1+dI8fXHHwKVWv3ae6Nse9r702S11/6CaenfcIIIAAAggggAACCCCwfAQi3bqZbbeNmyRglEXvf9AixxxV5xNH3YzhlddcZ1YWsbIj6z/ZT507kGYHjXjTDOrK/NSM55rtXbOPBzOmK2NUo9iUBaoJjvr27ZvmiNVXq45mshYs79OnT3zUXTD6LlXAUdmiqZomW0rWdE1qmkW9rgHGoLbnlClTkh3aNOmRAsiqc7rDDjvYs88+64Of7V0ZA9U2DYbfBzs/88wz/mWqYG2wXSE+F25othC16BMCCCDQAAIaEeB+3riMTXOfXNbvBJ9+msn+zd3wg35WWbm3VVSc5j4NvMYWL37UDfX4r3tMcBmpC6x9+wXuk70FdvDBC+zccxe4mf4WuJo6C+zTTxfYpEnfZXIStkEAAQQQQAABBBBAAIECECi/+nLfi8rB51n0q8l17lHloJNdtsZMixx6sEV6b1jn/ZfHDho+rpna1VTj89///ne10264YazfGtqdrN1999223nrrmWaET2yqM5qs5qVmZVfr129Z0skKVcP6kgUzNXz9u+++8/sk+0cz1SdrTzzxhF9c18Ctdlp//fX9vu+9956pFEBiU8kAlQUIJlpSFq3aHnvsUSP4qeVBvdNkHlpfyI0AaCHfHfqGAAKNRmD33VUTxew//6nfJbsP7Hx75JE5rm7LL3bzzXPtssvMBg0y23VXcz/UzdWMSX8OjYT/v/8zu/dezZRodswxZq72t6nGqBvh4YZeqJC22X77mZ11lrlfNsz0YeDYsbnJZE3fQ7ZAAAEEEEAAAQQQQACBTAQi/Te3yLlnu8Fec61i1z0t+sWkTHazqKuvWXHK6RZ97Emzbmta2fXXZLRfvjY6+uijbccdd/SnP++88+ynn1zN0qo2ZMgQ/+pf//qXmzD2nWCxf1aG5VnujxpNgNRNGbMJTbOXX355LIgcrNKcCMOGDfND0o866qhgsfXq1cu/fu655yyov6kFGop+4IEHJg2kBjtroqELL7wweOufx4wZ42d/11D/iy66qNq6TN4oOLvLLrvYvHnzTCbhzE0FZC+44AJ/GPVNTUPc1UaNGlWjry+//LJdffXVfr0yR4utNSm2DtNfBBBAoBQFDjrIXLDS7NprzY491lxdmrpf5ejR5oZ6mGmSvR120Ax7lX7YR5cuNY/lfpex//3PVQf9ofpDoyu0bOrUmvuEl+jnnYKdeqRqmkhxgw1i/VGfEh+huuKpDsFyBBBAAAEEEEAAAQQQyIFAmav/Wfntd6ZanhVbbGNllw6xyInHWyTFHx7RseOs4ozBZu+6PzI6d7Ly55+2iBtGXujtjjvusN69e/sZ4k855RSXYBLLMNl+++3txBNP9JmhGuq9n8vk0JB5BTJfe+01P4lR//7945mQ4ets1aqVSwq52P773/+ajqOAqGpkKgio7MxwBqhmjF/HZY0omKnh4wo+tnU1z15//XWfgbnbbrvZiBEjwoePv9Z+l7nsFWVrhs+juqEKjCabYT2+cy0vbrjhBttqq61MNsrgVJ8U9NVwdk3epIlwzzjjDH8EBUKvdX+Uani/PPbee28/IZQyQ//PZchouL8mRlLwVEFVDZUvlkYAtFjuFP1EAIGSFvjTn8wGDDB76SVzn+yZudrcdWru54/7gR7b5dJLXXmeMhf+rEx9CPcBovvhFXuk2mr2bLPx45MHSBUk1TlraxrOX9uQfmWSut9NagRGg0DpSivVdnTWIYAAAgggkFxgkBv2MHHixHg9tmRbhSfEmDx5sv9jNdl24WX6o3Do0KHhRbxGAAEEikZAkx+VPfawVa6ztpsM6Wo/M7y5Z01qFNl0EzNlTbhan/b1N1b58itmH3zohqi5y+u7sZU/+4RFUsyEXmgAqmWpbE9lOyoLU8PUFexUu/32232w79xzz/WBy2BoueY4OP300/1+yWZYP/jgg23ttdf2wcmRI0f6OpxbbLGFnXDCCfFjBw6qO6pgobJRFVgNhslrKLrqa2pZqgCojqem/quGqdpK7o8iBTAPOeQQ/z6bfzS0/38u+0U/HzWc/4MPPvCHUVaprvuSSy6xYHZ3lQpQmYC//e1vPjisALGa+qHAqILKqqOqYKiuJ5z96jcs4H8i7oe/vqRpRSDwyy+/+Nm2VNdCnxrQEECgtATc319+WLlKs9xyi7kfLpldnyYfdHW+ffBUIz7cz1RXT3S6H96gH8BdkqWAZnboWrdKlkUaZJBmkkVa68GrVq66qurWpA6SkkWaiSLbIIAAAsUrcNddd/k/2HQFGrZ49tluCGeKpgknNMGF6pYFf7Cl2DSrxaqRduedd2a1LzshgEDhCSijT4EgDettbC06/n9WecEQiyrQubQi+eV372Zl//i7nzQp4iYZKrWm+MqkSZNMEyRpciHN5J7YrrvuOhs8eLAdd9xxpp9HGsau7MeuXbv6SZUSt098r+zKCRMm+AmGNAlTpk3nGe8yUZRdqb7lsimbVB8SajIlDddPdt06n/qgLNGpbmiggsqraHhfkTcyQIv8BtJ9BBAoHYGePc19EqmC02annmpuwiGzK680W3HF1Nc4bpy5H8jm6svE6nNqf2V/Lo+WSRap+72i2lD7cIA0kyxSDcWvbTi+apGqnnmQNZr4LLsS/H1tedxezoEAAggUrYD+qNMHgMHsv7m6kLrOvJur83IcBBBAINcCmsio/LmnLeqGfEVfec0UEFV9UGvezCKuBmbkz9tbZOONcn3agjreiu4PhS233LJOfdIM73WZiKhNmzZ+GHmdTuI21nk01LwhWnOXQZLJNagPPXr08I+G6Ec+jkkANB/qnBMBBBBIIbDTTrHJh/bd19ynjLGAqEY7KCiqAKlqUk+frtn3zA1NiGV9Ko/flbGxJ58069gxxYHztFgByO22S31yl6zjPhVdNsw+MUBaW/BTR1240OxDNzpHj1RNEzclBkaD9xrJk6LsUarDsRwBBBBAoMAFnn/+eZ/RoqweGgIIIIBAaoGI++MhcshBqTdgDQIlJEAAtIRuJpeCAAKlIbD11rGsSdXy1Eg7V6rGP5JdnUYiaEJDZYEWY6Zj06bpa5G60YzxIGligDSTLFJXf9wVIU+mF1vmapqnnKxJAVIFlYvRNvUVswYBBBBAAAEEEEAAAQQQaFwCBEAb1/3mahFAoEgENAGQ6oAquPnCC8r0/MPN0ufSPq2lqwVTaYcfvonPClXmZ6lnMKpcTm1ZpIsXm6tjE8siTRYgTZdFqhqq2WaRKkCqbNJSvwdF8t+GbiKAAAIIIIAAAgggkDMB1cg8wE22sOmmm+bsmBwofwIEQPNnz5kRQACBtAKdO5sdc4yGwM93AdAt/fYbb7ytDRv2dtp9G8sGCj6mm9F+5sxYkDRZgDQXWaStWyefrCkIkJJF2li+GrlOBBBAAAEEEEAAgVIR0KR6etBKQ4AAaGncR64CAQQQQKAWAQWSM8kiTRUgTZdF+vvvtWeRurk4bO21a9YiDQKkZJHWcvNYhQACCCCAAAIIIIAAAgjUU4AAaD0B2R0BBBBAoPgFMskinTFj2VB7ZY0mPhQETdU0UVW6WqTJskjDAVJNKOUmNqYhgAACCCCAAAIIIIAAAgjUUYAAaB3B2BwBBBBAoHEKqC6rHqnaokW1B0gbIos0HCAlizTVnWE5AggggAACCCCAAAIINHYBAqCN/SuA60cAAQQQyIlA8+ZmffvGHqkOOH262eef18weDbJJ65tF2qaN2XrrLRtqnxggJYs01Z1hOQIIIIAAAggggAACCJSyAAHQUr67XBsCCCCAQEEJrLyymR6p2h9/1B4gTZdF+ttvtdci1RD6nj1TB0jJIk11Z1iOAAIIIIAAAggggAACxSxAALSY7x59RwABBBAoKYEWLdJnkf78s9kXXyTPItUkTgsWpCaprExfi7RtW7N1140FSRMzSBUgJYs0tS9rEEAAAQQQQAABBBBAoDAFCIAW5n2hVwgggAACCCQV6NLFTI9UrbYsUgVIZ80yW7Ik1d5mv/6aeRZpsgApWaSpbVmDAAIIIIAAAggggAAC+REgAJofd86KAAIIIIBAgwhkkkX6zTepM0hVj7S+WaTt2pn16mWWKkBKFmmD3HoOigACCCCAAAIIIIAAAikECICmgGExAggggAACpSrQo4eZHqnawoXJh9krg1QB0nRZpPPn155FWl5uttZaqQOkZJGmujMsRwABBBBAAAEEEEAAgWwECIBmo8Y+CCCAAAIIlLBAy5bpa5EmyyINAqTpskgrKtLXIm3fPpZFqmBosgdZpCX8BcilIYAAAggggAACCCCQYwECoDkG5XAIIIAAAgg0BoF0WaQaRj9pUvWh9uEA6cyZZkuXppaaN8/sgw9ij2RbKYs0PKN9siBps2bJ9mQZAggggAACCCCAAAIINDYBAqCN7Y5zvQgggAACCCwHgVatas8ijUbNvv02dYA0kyxSBVj1SNXIIk0lw3IEEEAAAQQQQAABBBqXAAHQxnW/uVoEEEAAAQQKQiASidUhra0WaWIWaTiDVAHShs4i1SROTZsWBBedQAABBBBAAAEEEEAAgXoIEACtBx67IoAAAggggEDDCdQ1izQxQJqLLNIVVjBbZ53kdUgVIFUtUgVzaQgggAACCCCAAAIIIFC4AgRAC/fe0DMEEEAAAQQQqEUgkyzS33+PTbikYGiyAGm6LNK5c2uvRdrE/SalGe2T1SBVgFTLySKt5SayCgEEEEAAAQQQQACB5SBAAHQ5IHMKBBBAAAEEEMiPQOvWmdciTRYgTZdFqomc0tUi7dDBbO21awZJgwApWaT5+drgrAgggAACCCCAAAKNR4AAaOO511wpAggggAACCCQIZJJF+ttvZl99lTyDNJNapHPmZJdFGgRIySJNuGm8RQABBBBAAAEEEECgjgIEQOsIxuYIIIAAAggg0LgE2rSpPYu0stLsu+9SB0hzkUXasaNZz57Vs0jDAVKySBvX1yRXiwACCCCAAAIIIFA3AQKgdfNiawQQQAABBBBAoJpAWVn6Ge2VRfrll2YKhiZ7zJhhVlFR7bDV3syeXXsWqeqM9uiROkBKFmk1Tt4ggAACCCCAAAIINDIBAqCN7IZzuQgggAACCCCw/AWURbrJJrFHsrMri/Tbb5MHR4OA6YIFyfaMLVuyJH0tUmWJhidsCmeQKkBKFmlqX9YggAACCCCAAAIIFLcAAdDivn/0HgEEEEAAAQRKQEBZpApO6pGq/fprrBZpEBBNfE6XRfrLL2Z6fPBB8jMkZpEmBkjJIk3uxlIEEEAAAQQQQACBwhcgAFr494geIoAAAggggAAC1rZt/bJINcv9woWpIeuaRZosQEoWaWpf1iCAAAIIIIAAAgjkT4AAaP7sOTMCCCCAAAIIIJAzgUyySOfPN5s8OflQewVIZ86svRZpuizSZs3MuneP1SJNFiBddVWzli1zdskcCAEEEEAAAQQQQ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" width="672" /></p>
<pre class="r"><code>####

###### Multiplot for Nonpartisan #####

#extract legend from houseCD (could be any b/c all same legend)
legP &lt;- get_legend(houseCDP)

#save earlier plots but remove their legends
sbCDP &lt;- sbCDP + theme(legend.position = &quot;none&quot;)
leoCDP &lt;- leoCDP + theme(legend.position = &quot;none&quot;)
sherCDP &lt;- sherCDP + theme(legend.position = &quot;none&quot;)
daCDP &lt;- daCDP + theme(legend.position = &quot;none&quot;)
houseCDP &lt;- houseCDP + theme(legend.position = &quot;none&quot;)

#arrange plots, and now the extracted legend is a defacto plot (for positioning)
multi_npContrastP &lt;- ggarrange(sbCDP, leoCDP, sherCDP,  daCDP, houseCDP, legP, 
                               ncol = 3, nrow = 2,
                               font.label=list(color=&quot;black&quot;,size=10))

annotate_figure(multi_npContrastP,
                top = text_grob(&quot;&quot;, 
                                color = &quot;black&quot;, face = &quot;bold&quot;, size = 14),
                bottom = text_grob(&quot;2016 County Presidential Vote&quot;, 
                                   color = &quot;black&quot;,
                                   hjust = 0.35, vjust = -0.75, size = 12),
                left = text_grob(&quot;Proportion&quot;, color = &quot;black&quot;, rot = 90),
                right = &quot;&quot;,
                fig.lab = &quot;&quot;, fig.lab.face = &quot;bold&quot;
)</code></pre>
<p><img 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" width="672" /></p>
<pre class="r"><code>#######################################################################################

### ONLINE APPENDIX: ANOVA, Chi-Square tests to see if pid*Pres_Vote is significant  ###

#######################################################################################

sbNP.interact &lt;- glm(RCO322_np ~ 
                       pid*Pres_Vote.f +
                       perceive_extreme_very.f +
                       polint.f +
                       educ.int.f +
                       birthyr,
                     family=&quot;binomial&quot;,
                     data = subset(sbGLM))

summary(sbNP.interact)</code></pre>
<pre><code>## 
## Call:
## glm(formula = RCO322_np ~ pid * Pres_Vote.f + perceive_extreme_very.f + 
##     polint.f + educ.int.f + birthyr, family = &quot;binomial&quot;, data = subset(sbGLM))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.8968  -1.1814   0.7582   0.9998   1.7380  
## 
## Coefficients:
##                                             Estimate Std. Error z value
## (Intercept)                                21.139031   6.470744   3.267
## pidRepublican                               0.128712   0.270907   0.475
## Pres_Vote.fLean Democrat                    0.076771   0.181295   0.423
## Pres_Vote.fLean Republican                  0.162092   0.197721   0.820
## Pres_Vote.fSolid Republican                 0.467710   0.249279   1.876
## perceive_extreme_very.fOutparty is extreme  0.135262   0.108461   1.247
## polint.fNews: Now and then                  0.214337   0.297197   0.721
## polint.fNews: Some of the time              0.632512   0.274144   2.307
## polint.fNews: Most of the time              1.080538   0.271159   3.985
## educ.int.fSome College                      0.261938   0.150894   1.736
## educ.int.f2-Year Degree                     0.102957   0.189569   0.543
## educ.int.f4-Year Degree                     0.340224   0.149075   2.282
## educ.int.fPost-Graduate                     0.532422   0.174447   3.052
## birthyr                                    -0.011066   0.003264  -3.391
## pidRepublican:Pres_Vote.fLean Democrat     -0.577880   0.331331  -1.744
## pidRepublican:Pres_Vote.fLean Republican   -0.785235   0.336506  -2.333
## pidRepublican:Pres_Vote.fSolid Republican  -1.147354   0.368529  -3.113
##                                            Pr(&gt;|z|)    
## (Intercept)                                0.001087 ** 
## pidRepublican                              0.634705    
## Pres_Vote.fLean Democrat                   0.671958    
## Pres_Vote.fLean Republican                 0.412330    
## Pres_Vote.fSolid Republican                0.060620 .  
## perceive_extreme_very.fOutparty is extreme 0.212361    
## polint.fNews: Now and then                 0.470789    
## polint.fNews: Some of the time             0.021042 *  
## polint.fNews: Most of the time             6.75e-05 ***
## educ.int.fSome College                     0.082580 .  
## educ.int.f2-Year Degree                    0.587052    
## educ.int.f4-Year Degree                    0.022476 *  
## educ.int.fPost-Graduate                    0.002273 ** 
## birthyr                                    0.000697 ***
## pidRepublican:Pres_Vote.fLean Democrat     0.081139 .  
## pidRepublican:Pres_Vote.fLean Republican   0.019622 *  
## pidRepublican:Pres_Vote.fSolid Republican  0.001850 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2189.3  on 1608  degrees of freedom
## Residual deviance: 2061.6  on 1592  degrees of freedom
##   (48 observations deleted due to missingness)
## AIC: 2095.6
## 
## Number of Fisher Scoring iterations: 4</code></pre>
<pre class="r"><code>#anova shows interaction is significant
anova(sbNP.interact, test=&quot;Chisq&quot;)</code></pre>
<pre><code>## Analysis of Deviance Table
## 
## Model: binomial, link: logit
## 
## Response: RCO322_np
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(&gt;Chi)    
## NULL                                     1608     2189.3              
## pid                      1   15.458      1607     2173.9 8.435e-05 ***
## Pres_Vote.f              3    0.484      1604     2173.4 0.9224317    
## perceive_extreme_very.f  1    7.952      1603     2165.4 0.0048039 ** 
## polint.f                 3   71.413      1600     2094.0 2.127e-15 ***
## educ.int.f               4    9.819      1596     2084.2 0.0435829 *  
## birthyr                  1   12.037      1595     2072.2 0.0005216 ***
## pid:Pres_Vote.f          3   10.562      1592     2061.6 0.0143471 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>sbNP.baseline &lt;- glm(RCO322_np ~ 
                       pid + Pres_Vote.f +
                       perceive_extreme_very.f +
                       polint.f +
                       educ.int.f +
                       birthyr,
                     family=&quot;binomial&quot;,
                     data = subset(sbGLM))

summary(sbNP.baseline)</code></pre>
<pre><code>## 
## Call:
## glm(formula = RCO322_np ~ pid + Pres_Vote.f + perceive_extreme_very.f + 
##     polint.f + educ.int.f + birthyr, family = &quot;binomial&quot;, data = subset(sbGLM))
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.8066  -1.1953   0.7750   0.9986   1.7082  
## 
## Coefficients:
##                                             Estimate Std. Error z value
## (Intercept)                                21.646611   6.441483   3.361
## pidRepublican                              -0.526846   0.112555  -4.681
## Pres_Vote.fLean Democrat                   -0.067746   0.151609  -0.447
## Pres_Vote.fLean Republican                 -0.075820   0.159065  -0.477
## Pres_Vote.fSolid Republican                -0.035191   0.172032  -0.205
## perceive_extreme_very.fOutparty is extreme  0.121564   0.107917   1.126
## polint.fNews: Now and then                  0.206102   0.296015   0.696
## polint.fNews: Some of the time              0.619672   0.272709   2.272
## polint.fNews: Most of the time              1.078880   0.269919   3.997
## educ.int.fSome College                      0.260301   0.149972   1.736
## educ.int.f2-Year Degree                     0.111519   0.188443   0.592
## educ.int.f4-Year Degree                     0.344906   0.148417   2.324
## educ.int.fPost-Graduate                     0.521717   0.173460   3.008
## birthyr                                    -0.011230   0.003249  -3.456
##                                            Pr(&gt;|z|)    
## (Intercept)                                0.000778 ***
## pidRepublican                              2.86e-06 ***
## Pres_Vote.fLean Democrat                   0.654988    
## Pres_Vote.fLean Republican                 0.633605    
## Pres_Vote.fSolid Republican                0.837915    
## perceive_extreme_very.fOutparty is extreme 0.259970    
## polint.fNews: Now and then                 0.486269    
## polint.fNews: Some of the time             0.023070 *  
## polint.fNews: Most of the time             6.41e-05 ***
## educ.int.fSome College                     0.082623 .  
## educ.int.f2-Year Degree                    0.553990    
## educ.int.f4-Year Degree                    0.020131 *  
## educ.int.fPost-Graduate                    0.002632 ** 
## birthyr                                    0.000548 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2189.3  on 1608  degrees of freedom
## Residual deviance: 2072.2  on 1595  degrees of freedom
##   (48 observations deleted due to missingness)
## AIC: 2100.2
## 
## Number of Fisher Scoring iterations: 4</code></pre>
<pre class="r"><code># Compare Nonpartisan SB models, with and w/o interaction
anova(sbNP.baseline, sbNP.interact, test=&quot;Chisq&quot;)</code></pre>
<pre><code>## Analysis of Deviance Table
## 
## Model 1: RCO322_np ~ pid + Pres_Vote.f + perceive_extreme_very.f + polint.f + 
##     educ.int.f + birthyr
## Model 2: RCO322_np ~ pid * Pres_Vote.f + perceive_extreme_very.f + polint.f + 
##     educ.int.f + birthyr
##   Resid. Df Resid. Dev Df Deviance Pr(&gt;Chi)  
## 1      1595     2072.2                       
## 2      1592     2061.6  3   10.562  0.01435 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>leoNP.interact &lt;- glm(RCO321_np ~ 
                        pid*Pres_Vote.f +
                        perceive_extreme_very.f +
                        polint.f +
                        educ.int.f +
                        birthyr,
                      family=&quot;binomial&quot;,
                      data = subset(leoGLM))

#anova shows interaction is significant
anova(leoNP.interact, test=&quot;Chisq&quot;)</code></pre>
<pre><code>## Analysis of Deviance Table
## 
## Model: binomial, link: logit
## 
## Response: RCO321_np
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(&gt;Chi)    
## NULL                                     1623     2244.2              
## pid                      1  16.9863      1622     2227.2 3.765e-05 ***
## Pres_Vote.f              3   0.8689      1619     2226.3  0.832926    
## perceive_extreme_very.f  1   1.5397      1618     2224.8  0.214658    
## polint.f                 3  23.3269      1615     2201.4 3.452e-05 ***
## educ.int.f               4   1.1412      1611     2200.3  0.887675    
## birthyr                  1   4.0362      1610     2196.3  0.044534 *  
## pid:Pres_Vote.f          3  13.8176      1607     2182.4  0.003164 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>leoNP.baseline &lt;- glm(RCO321_np ~ 
                        pid + Pres_Vote.f +
                        perceive_extreme_very.f +
                        polint.f +
                        educ.int.f +
                        birthyr,
                      family=&quot;binomial&quot;,
                      data = subset(leoGLM))

# Compare Nonpartisan LEO models, with and w/o interaction
anova(leoNP.baseline, leoNP.interact, test=&quot;Chisq&quot;)</code></pre>
<pre><code>## Analysis of Deviance Table
## 
## Model 1: RCO321_np ~ pid + Pres_Vote.f + perceive_extreme_very.f + polint.f + 
##     educ.int.f + birthyr
## Model 2: RCO321_np ~ pid * Pres_Vote.f + perceive_extreme_very.f + polint.f + 
##     educ.int.f + birthyr
##   Resid. Df Resid. Dev Df Deviance Pr(&gt;Chi)   
## 1      1610     2196.3                        
## 2      1607     2182.4  3   13.818 0.003164 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>daNP.interact &lt;- glm(RCO324_np ~ 
                       pid*Pres_Vote.f +
                       perceive_extreme_very.f +
                       polint.f +
                       educ.int.f +
                       birthyr,
                     family=&quot;binomial&quot;,
                     data = subset(daGLM))

anova(daNP.interact, test=&quot;Chisq&quot;)</code></pre>
<pre><code>## Analysis of Deviance Table
## 
## Model: binomial, link: logit
## 
## Response: RCO324_np
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(&gt;Chi)    
## NULL                                     1620     2247.2              
## pid                      1    4.973      1619     2242.2 0.0257474 *  
## Pres_Vote.f              3    0.503      1616     2241.7 0.9182933    
## perceive_extreme_very.f  1    4.256      1615     2237.4 0.0391233 *  
## polint.f                 3   51.322      1612     2186.1 4.176e-11 ***
## educ.int.f               4    6.403      1608     2179.7 0.1709911    
## birthyr                  1   10.922      1607     2168.8 0.0009505 ***
## pid:Pres_Vote.f          3   14.253      1604     2154.6 0.0025805 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>daNP.baseline &lt;- glm(RCO324_np ~ 
                       pid + Pres_Vote.f +
                       perceive_extreme_very.f +
                       polint.f +
                       educ.int.f +
                       birthyr,
                     family=&quot;binomial&quot;,
                     data = subset(daGLM))

# Compare Nonpartisan DA models, with and w/o interaction
anova(daNP.baseline, daNP.interact, test=&quot;Chisq&quot;)</code></pre>
<pre><code>## Analysis of Deviance Table
## 
## Model 1: RCO324_np ~ pid + Pres_Vote.f + perceive_extreme_very.f + polint.f + 
##     educ.int.f + birthyr
## Model 2: RCO324_np ~ pid * Pres_Vote.f + perceive_extreme_very.f + polint.f + 
##     educ.int.f + birthyr
##   Resid. Df Resid. Dev Df Deviance Pr(&gt;Chi)   
## 1      1607     2168.8                        
## 2      1604     2154.6  3   14.253  0.00258 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>sherNP.interact &lt;- glm(RCO323_np ~ 
                         pid*Pres_Vote.f +
                         perceive_extreme_very.f +
                         polint.f +
                         educ.int.f +
                         birthyr,
                       family=&quot;binomial&quot;,
                       data = subset(sherGLM))

anova(sherNP.interact, test=&quot;Chisq&quot;)</code></pre>
<pre><code>## Analysis of Deviance Table
## 
## Model: binomial, link: logit
## 
## Response: RCO323_np
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev  Pr(&gt;Chi)    
## NULL                                     1624     2250.3              
## pid                      1   4.0433      1623     2246.2 0.0443466 *  
## Pres_Vote.f              3   5.6591      1620     2240.6 0.1294283    
## perceive_extreme_very.f  1   4.4044      1619     2236.2 0.0358474 *  
## polint.f                 3  31.3709      1616     2204.8 7.101e-07 ***
## educ.int.f               4   5.5480      1612     2199.3 0.2355433    
## birthyr                  1  11.0781      1611     2188.2 0.0008735 ***
## pid:Pres_Vote.f          3  13.9080      1608     2174.3 0.0030331 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>sherNP.baseline &lt;- glm(RCO323_np ~ 
                         pid + Pres_Vote.f +
                         perceive_extreme_very.f +
                         polint.f +
                         educ.int.f +
                         birthyr,
                       family=&quot;binomial&quot;,
                       data = subset(sherGLM))

# Compare Nonpartisan LEO models, with and w/o interaction
anova(sherNP.baseline, sherNP.interact, test=&quot;Chisq&quot;)</code></pre>
<pre><code>## Analysis of Deviance Table
## 
## Model 1: RCO323_np ~ pid + Pres_Vote.f + perceive_extreme_very.f + polint.f + 
##     educ.int.f + birthyr
## Model 2: RCO323_np ~ pid * Pres_Vote.f + perceive_extreme_very.f + polint.f + 
##     educ.int.f + birthyr
##   Resid. Df Resid. Dev Df Deviance Pr(&gt;Chi)   
## 1      1611     2188.2                        
## 2      1608     2174.3  3   13.908 0.003033 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>houseNP.interact &lt;- glm(RCO327_np ~ 
                          pid*Pres_Vote.f +
                          perceive_extreme_very.f +
                          polint.f +
                          educ.int.f +
                          birthyr,
                        family=&quot;binomial&quot;,
                        data = subset(houseGLM))

anova(houseNP.interact, test=&quot;Chisq&quot;)</code></pre>
<pre><code>## Analysis of Deviance Table
## 
## Model: binomial, link: logit
## 
## Response: RCO327_np
## 
## Terms added sequentially (first to last)
## 
## 
##                         Df Deviance Resid. Df Resid. Dev Pr(&gt;Chi)   
## NULL                                     1620     2168.8            
## pid                      1   5.8918      1619     2162.9  0.01521 * 
## Pres_Vote.f              3   4.2411      1616     2158.7  0.23658   
## perceive_extreme_very.f  1   0.1876      1615     2158.5  0.66495   
## polint.f                 3   6.2406      1612     2152.2  0.10048   
## educ.int.f               4   6.2541      1608     2146.0  0.18096   
## birthyr                  1   3.1648      1607     2142.8  0.07524 . 
## pid:Pres_Vote.f          3  14.5544      1604     2128.3  0.00224 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>houseNP.baseline &lt;- glm(RCO327_np ~ 
                          pid + Pres_Vote.f +
                          perceive_extreme_very.f +
                          polint.f +
                          educ.int.f +
                          birthyr,
                        family=&quot;binomial&quot;,
                        data = subset(houseGLM))

# Compare Nonpartisan LEO models, with and w/o interaction
anova(houseNP.baseline, houseNP.interact, test=&quot;Chisq&quot;)</code></pre>
<pre><code>## Analysis of Deviance Table
## 
## Model 1: RCO327_np ~ pid + Pres_Vote.f + perceive_extreme_very.f + polint.f + 
##     educ.int.f + birthyr
## Model 2: RCO327_np ~ pid * Pres_Vote.f + perceive_extreme_very.f + polint.f + 
##     educ.int.f + birthyr
##   Resid. Df Resid. Dev Df Deviance Pr(&gt;Chi)   
## 1      1607     2142.8                        
## 2      1604     2128.3  3   14.554  0.00224 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<pre class="r"><code>#######################################################################################

### ONLINE APPENDIX: Models of U.S. House with pres_vote at congressional district level  ###

#######################################################################################

#create pres_Vote categories based on presidential vote at the congressional district

houseMarginsNP2$cd_presvote &lt;- ifelse(houseMarginsNP2$trump2020 &gt;=65, &quot;Solid Republican&quot;,
                                      ifelse(houseMarginsNP2$trump2020 &gt;=50 &amp; houseMarginsNP2$trump2020 &lt; 65, &quot;Lean Republican&quot;,
                                             ifelse(houseMarginsNP2$trump2020 &lt;50 &amp; houseMarginsNP2$trump2020 &gt;=35, &quot;Lean Democrat&quot;,
                                                    ifelse(houseMarginsNP2$trump2020 &lt;35, &quot;Solid Democrat&quot;, NA))))

sbMarginsNP2$cd_presvote &lt;- ifelse(sbMarginsNP2$trump2020 &gt;=65, &quot;Solid Republican&quot;,
                                   ifelse(sbMarginsNP2$trump2020 &gt;=50 &amp; sbMarginsNP2$trump2020 &lt; 65, &quot;Lean Republican&quot;,
                                          ifelse(sbMarginsNP2$trump2020 &lt;50 &amp; sbMarginsNP2$trump2020 &gt;=35, &quot;Lean Democrat&quot;,
                                                 ifelse(sbMarginsNP2$trump2020 &lt;35, &quot;Solid Democrat&quot;, NA))))

leoMarginsNP2$cd_presvote &lt;- ifelse(leoMarginsNP2$trump2020 &gt;=65, &quot;Solid Republican&quot;,
                                    ifelse(leoMarginsNP2$trump2020 &gt;=50 &amp; leoMarginsNP2$trump2020 &lt; 65, &quot;Lean Republican&quot;,
                                           ifelse(leoMarginsNP2$trump2020 &lt;50 &amp; leoMarginsNP2$trump2020 &gt;=35, &quot;Lean Democrat&quot;,
                                                  ifelse(leoMarginsNP2$trump2020 &lt;35, &quot;Solid Democrat&quot;, NA))))

sherMarginsNP2$cd_presvote &lt;- ifelse(sherMarginsNP2$trump2020 &gt;=65, &quot;Solid Republican&quot;,
                                     ifelse(sherMarginsNP2$trump2020 &gt;=50 &amp; sherMarginsNP2$trump2020 &lt; 65, &quot;Lean Republican&quot;,
                                            ifelse(sherMarginsNP2$trump2020 &lt;50 &amp; sherMarginsNP2$trump2020 &gt;=35, &quot;Lean Democrat&quot;,
                                                   ifelse(sherMarginsNP2$trump2020 &lt;35, &quot;Solid Democrat&quot;, NA))))

daMarginsNP2$cd_presvote &lt;- ifelse(daMarginsNP2$trump2020 &gt;=65, &quot;Solid Republican&quot;,
                                   ifelse(daMarginsNP2$trump2020 &gt;=50 &amp; daMarginsNP2$trump2020 &lt; 65, &quot;Lean Republican&quot;,
                                          ifelse(daMarginsNP2$trump2020 &lt;50 &amp; daMarginsNP2$trump2020 &gt;=35, &quot;Lean Democrat&quot;,
                                                 ifelse(daMarginsNP2$trump2020 &lt;35, &quot;Solid Democrat&quot;, NA))))



Finter.house.cd.SolidD &lt;- glm(RCO327_np ~ 
                                pid +
                                perceive_extreme_very.f +
                                polint.f +
                                educ.int.f +
                                birthyr,
                              family=&quot;binomial&quot;,
                              data = subset(houseMarginsNP2, cd_presvote==&quot;Solid Democrat&quot;))

Finter.house.cd.LeanD &lt;- glm(RCO327_np ~ 
                               pid +
                               perceive_extreme_very.f +
                               polint.f +
                               educ.int.f +
                               birthyr,
                             family=&quot;binomial&quot;,
                             data = subset(houseMarginsNP2, cd_presvote==&quot;Lean Democrat&quot;))

Finter.house.cd.LeanR &lt;- glm(RCO327_np ~ 
                               pid +
                               perceive_extreme_very.f +
                               polint.f +
                               educ.int.f +
                               birthyr,
                             family=&quot;binomial&quot;,
                             data = subset(houseMarginsNP2, cd_presvote==&quot;Lean Republican&quot;))

Finter.house.cd.SolidR &lt;- glm(RCO327_np ~ 
                                pid +
                                perceive_extreme_very.f +
                                polint.f +
                                educ.int.f +
                                birthyr,
                              family=&quot;binomial&quot;,
                              data = subset(houseMarginsNP2, cd_presvote==&quot;Solid Republican&quot;))


pl &lt;- c(
  `pidRepublican` = &quot;Party ID&quot;,
  `polint.f4` = &quot;News: Most of the time&quot;,
  `educ.int.f5` = &quot;Education: Post-graduate&quot;,
  `perceive_extreme_very.f1` = &quot;Out-party is very extreme&quot;)

tab_model(Finter.house.cd.SolidD, Finter.house.cd.LeanD, Finter.house.cd.LeanR, Finter.house.cd.SolidR,
          terms=c(&quot;pidRepublican&quot;,
                  &quot;polint.f4&quot;,
                  &quot;educ.int.f5&quot;,
                  &quot;perceive_extreme_very.f1&quot;),
          dv.labels = c(&quot;Solid Democrat&quot;,&quot;Lean Democrat&quot;,
                        &quot;Lean Republican&quot;,&quot;Solid Republican&quot;),
          pred.labels = pl,
          collapse.se=TRUE,
          show.ci=FALSE,
          p.style=&quot;stars&quot;,
          transform=NULL,
          show.aic=TRUE)</code></pre>
<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">
 
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Republican
</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">
Predictors
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Party ID
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.36 <sup></sup><br>(0.28)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.03 <sup></sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.44 <sup>*</sup><br>(0.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.81 <sup>**</sup><br>(0.30)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Out-party is very extreme
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.05 <sup></sup><br>(0.25)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.16 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.33 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.50 <sup></sup><br>(0.28)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Most of the time
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.38 <sup></sup><br>(0.54)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.44 <sup></sup><br>(0.58)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.72 <sup></sup><br>(0.51)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.31 <sup></sup><br>(0.71)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: Post-graduate
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.51 <sup></sup><br>(0.39)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.33 <sup></sup><br>(0.30)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.67 <sup>*</sup><br>(0.29)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-1.09 <sup>*</sup><br>(0.55)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">
Observations
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
335
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
492
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
552
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
235
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
R<sup>2</sup> Tjur
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.044
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.013
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.046
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.080
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
AIC
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
440.347
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
683.171
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
735.271
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
317.868
</td>
</tr>
<tr>
<td colspan="5" style="font-style:italic; border-top:double black; text-align:right;">
<ul>
<li>p&lt;0.05   ** p&lt;0.01   *** p&lt;0.001
</td>
</tr></li>
</ul>
</table>
<pre class="r"><code>###### Intra-party tables ####
## Intraparty - Dem full models
levels(sbMarginsNP2$polint.f)</code></pre>
<pre><code>## [1] &quot;1&quot; &quot;2&quot; &quot;3&quot; &quot;4&quot;</code></pre>
<pre class="r"><code>levels(sbMarginsNP2$educ.int.f)</code></pre>
<pre><code>## [1] &quot;1&quot; &quot;2&quot; &quot;3&quot; &quot;4&quot; &quot;5&quot;</code></pre>
<pre class="r"><code>houseMarginsNP2$cd_presvote &lt;- as.factor(houseMarginsNP2$cd_presvote)
levels(houseMarginsNP2$cd_presvote)</code></pre>
<pre><code>## [1] &quot;Lean Democrat&quot;    &quot;Lean Republican&quot;  &quot;Solid Democrat&quot;   &quot;Solid Republican&quot;</code></pre>
<pre class="r"><code>sbMarginsNP2$cd_presvote &lt;- as.factor(sbMarginsNP2$cd_presvote)
leoMarginsNP2$cd_presvote &lt;- as.factor(leoMarginsNP2$cd_presvote)
sherMarginsNP2$cd_presvote &lt;- as.factor(sherMarginsNP2$cd_presvote)
daMarginsNP2$cd_presvote &lt;- as.factor(daMarginsNP2$cd_presvote)

sbMarginsNP2 &lt;- within(sbMarginsNP2, polint.f &lt;- relevel(polint.f, ref = &quot;1&quot;))
sbMarginsNP2 &lt;- within(sbMarginsNP2, educ.int.f &lt;- relevel(educ.int.f, ref = &quot;1&quot;))
sbMarginsNP2 &lt;- within(sbMarginsNP2, cd_presvote &lt;- relevel(cd_presvote, ref = &quot;Solid Democrat&quot;))

leoMarginsNP2 &lt;- within(leoMarginsNP2, polint.f &lt;- relevel(polint.f, ref = &quot;1&quot;))
leoMarginsNP2 &lt;- within(leoMarginsNP2, educ.int.f &lt;- relevel(educ.int.f, ref = &quot;1&quot;))
leoMarginsNP2 &lt;- within(leoMarginsNP2, cd_presvote &lt;- relevel(cd_presvote, ref = &quot;Solid Democrat&quot;))

sherMarginsNP2 &lt;- within(sherMarginsNP2, polint.f &lt;- relevel(polint.f, ref = &quot;1&quot;))
sherMarginsNP2 &lt;- within(sherMarginsNP2, educ.int.f &lt;- relevel(educ.int.f, ref = &quot;1&quot;))
sherMarginsNP2 &lt;- within(sherMarginsNP2, cd_presvote &lt;- relevel(cd_presvote, ref = &quot;Solid Democrat&quot;))

daMarginsNP2 &lt;- within(daMarginsNP2, polint.f &lt;- relevel(polint.f, ref = &quot;1&quot;))
daMarginsNP2 &lt;- within(daMarginsNP2, educ.int.f &lt;- relevel(educ.int.f, ref = &quot;1&quot;))
daMarginsNP2 &lt;- within(daMarginsNP2, cd_presvote &lt;- relevel(cd_presvote, ref = &quot;Solid Democrat&quot;))

houseMarginsNP2 &lt;- within(houseMarginsNP2, polint.f &lt;- relevel(polint.f, ref = &quot;1&quot;))
houseMarginsNP2 &lt;- within(houseMarginsNP2, educ.int.f &lt;- relevel(educ.int.f, ref = &quot;1&quot;))
houseMarginsNP2 &lt;- within(houseMarginsNP2, cd_presvote &lt;- relevel(cd_presvote, ref = &quot;Solid Democrat&quot;))


# Full Model (SB)
glm.sb2.cdD &lt;- glm(RCO322_np ~ 
                     perceive_extreme_very.f +
                     polint.f +
                     educ.int.f +
                     birthyr +
                     cd_presvote,
                   family=&quot;binomial&quot;,
                   data = subset(sbMarginsNP2, pid==&quot;Democrat&quot;))

# Full Model (LEO)
glm.leo2.cdD &lt;- glm(RCO321_np ~ 
                      perceive_extreme_very.f +
                      polint.f +
                      educ.int.f +
                      birthyr +
                      cd_presvote,
                    family=&quot;binomial&quot;,
                    data = subset(leoMarginsNP2, pid==&quot;Democrat&quot;))

# Full Model (SHER)
glm.sher2.cdD &lt;- glm(RCO323_np ~ 
                       perceive_extreme_very.f +
                       polint.f +
                       educ.int.f +
                       birthyr +
                       cd_presvote,
                     family=&quot;binomial&quot;,
                     data = subset(sherMarginsNP2, pid==&quot;Democrat&quot;))

# Full Model (DA)
glm.da2.cdD &lt;- glm(RCO324_np ~ 
                     perceive_extreme_very.f +
                     polint.f +
                     educ.int.f +
                     birthyr +
                     cd_presvote,
                   family=&quot;binomial&quot;,
                   data = subset(daMarginsNP2, pid==&quot;Democrat&quot;))

# Full Model (HOUSE)
glm.house2.cdD &lt;- glm(RCO327_np ~ 
                        perceive_extreme_very.f +
                        polint.f +
                        educ.int.f +
                        birthyr +
                        cd_presvote,
                      family=&quot;binomial&quot;,
                      data = subset(houseMarginsNP2, pid==&quot;Democrat&quot;))

pLintraCD &lt;- c(
  `polint.f2` = &quot;News: Now and then&quot;,
  `polint.f3` = &quot;News: Some of the time&quot;,
  `polint.f4` = &quot;News: Most of the time&quot;,
  `educ.int.f2` = &quot;Education: Some college&quot;,
  `educ.int.f3` = &quot;Education: 2-year degree&quot;,
  `educ.int.f4` = &quot;Education: 4-year degree&quot;,
  `educ.int.f5` = &quot;Education: Post-graduate&quot;,
  `perceive_extreme_very.f1` = &quot;Out-party is very extreme&quot;,
  `cd_presvoteLean Democrat` = &quot;House District: Lean Democrat&quot;,
  `cd_presvoteLean Republican` = &quot;House District: Lean Republican&quot;,
  `cd_presvoteSolid Republican` = &quot;House District: Solid Republican&quot;)


# Logit 
tab_model(glm.sb2.cdD, glm.leo2.cdD, glm.sher2.cdD, glm.da2.cdD, glm.house2.cdD,
          show.reflvl = TRUE)</code></pre>
<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">
 
</th>
<th colspan="3" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
RCO322_np
</th>
<th colspan="3" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
RCO321_np
</th>
<th colspan="3" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
RCO323_np
</th>
<th colspan="3" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
RCO324_np
</th>
<th colspan="3" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
RCO327_np
</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">
Predictors
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Odds Ratios
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
CI
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
p
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Odds Ratios
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
CI
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col7">
p
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col8">
Odds Ratios
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col9">
CI
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  0">
p
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  1">
Odds Ratios
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  2">
CI
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  3">
p
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  4">
Odds Ratios
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  5">
CI
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  6">
p
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
(Intercept)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
5939559275.61
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
86.53 – 474424336834575744.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
<strong>0.015</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
114.77
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.00 – 1509462324.43
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.570
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
120812.60
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.01 – 2245416982577.76
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.170
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
20405502150.07
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
1065.07 – 442735598322732736.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
<strong>0.006</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
5.61
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.00 – 80368048.39
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.837
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
birthyr
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.99
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.98 – 1.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
<strong>0.010</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.99 – 1.01
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.479
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
0.99
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.99 – 1.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.133
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
0.99
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.98 – 1.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
<strong>0.003</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.99 – 1.01
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.738
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Solid Democrat
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
<em>Reference</em>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
<em>Reference</em>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
<em>Reference</em>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
<em>Reference</em>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
<em>Reference</em>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Lean Democrat
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.39
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.96 – 2.02
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.086
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.13
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.80 – 1.60
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.476
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
1.56
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
1.10 – 2.22
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
<strong>0.013</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
1.12
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.79 – 1.60
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.524
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.17
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.82 – 1.66
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.387
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Lean Republican
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.66
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.14 – 2.44
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
<strong>0.009</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.47
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.04 – 2.09
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
<strong>0.030</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
1.74
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
1.22 – 2.48
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
<strong>0.002</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
1.28
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.90 – 1.83
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.172
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.32
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.93 – 1.88
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.116
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Solid Republican
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.80
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.06 – 3.08
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
<strong>0.030</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.26
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.78 – 2.05
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.343
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
1.97
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
1.20 – 3.26
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
<strong>0.007</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
1.50
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.91 – 2.48
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.115
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.66
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
1.03 – 2.69
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
<strong>0.039</strong>
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
educ.int.f2
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.33
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.87 – 2.03
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.193
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.09
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.73 – 1.63
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.658
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
0.74
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.49 – 1.11
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.151
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
0.92
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.61 – 1.38
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.672
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.04
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.70 – 1.56
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.848
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
educ.int.f3
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.26
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.74 – 2.17
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.399
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.85
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.52 – 1.41
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.533
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
0.94
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.56 – 1.57
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.809
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
1.13
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.68 – 1.89
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.641
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.21
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.73 – 2.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.448
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
educ.int.f4
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.47
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.98 – 2.22
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.064
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.86
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.58 – 1.25
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.428
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
0.94
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.64 – 1.38
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.750
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
0.90
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.61 – 1.32
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.583
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.10
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.75 – 1.62
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.629
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
educ.int.f5
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
2.45
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.53 – 3.97
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
<strong>&lt;0.001</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.22
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.80 – 1.85
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.354
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
1.05
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.69 – 1.62
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.807
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
1.43
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.93 – 2.21
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.103
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
0.99
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.65 – 1.50
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.956
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
perceive_extreme_very.f1
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.44
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.07 – 1.95
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
<strong>0.018</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.20
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.91 – 1.58
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.187
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
1.40
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
1.06 – 1.85
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
<strong>0.019</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
1.29
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.97 – 1.70
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.079
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
0.96
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.72 – 1.26
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.748
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
polint.f2
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.34
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.66 – 2.82
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.424
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
2.18
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.07 – 4.63
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
<strong>0.037</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
1.52
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.75 – 3.15
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.248
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
1.98
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.93 – 4.40
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.083
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.66
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.82 – 3.49
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.168
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
polint.f3
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
2.05
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.07 – 4.07
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
<strong>0.035</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
2.31
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.20 – 4.67
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
<strong>0.015</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
1.88
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.99 – 3.68
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.057
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
2.84
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
1.43 – 5.99
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
<strong>0.004</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.70
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.89 – 3.38
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.118
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
polint.f4
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
4.19
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
2.19 – 8.30
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
<strong>&lt;0.001</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
3.13
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.63 – 6.30
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
<strong>0.001</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
2.86
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
1.51 – 5.57
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
<strong>0.001</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
4.32
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
2.19 – 9.09
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
<strong>&lt;0.001</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.74
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.91 – 3.44
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.101
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">
Observations
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="3">
904
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="3">
921
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="3">
917
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="3">
916
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="3">
918
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
R<sup>2</sup> Tjur
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="3">
0.131
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="3">
0.035
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="3">
0.058
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="3">
0.078
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="3">
0.010
</td>
</tr>
</table>
<pre class="r"><code>tab_model(glm.sb2.cdD, glm.leo2.cdD, glm.sher2.cdD, glm.da2.cdD, glm.house2.cdD,
          transform = NULL,
          show.ci=FALSE,
          p.style = &quot;stars&quot;,
          collapse.se=TRUE,
          show.aic=TRUE,
          show.reflvl = TRUE,
          dv.labels = c(&quot;School Board&quot;,&quot;L.E.O.&quot;,
                        &quot;Sheriff&quot;,&quot;D.A.&quot;,&quot;U.S. House&quot;),
          pred.labels = pLintraCD)</code></pre>
<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">
 
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
School Board
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
L.E.O.
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Sheriff
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
D.A.
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
U.S. House
</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">
Predictors
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
(Intercept)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
22.50 <sup>*</sup><br>(9.24)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
4.74 <sup></sup><br>(8.36)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
11.70 <sup></sup><br>(8.52)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
23.74 <sup>**</sup><br>(8.58)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.72 <sup></sup><br>(8.40)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Out-party is very extreme
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.36 <sup>*</sup><br>(0.15)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.18 <sup></sup><br>(0.14)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.33 <sup>*</sup><br>(0.14)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.25 <sup></sup><br>(0.14)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.05 <sup></sup><br>(0.14)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Now and then
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.30 <sup></sup><br>(0.37)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.78 <sup>*</sup><br>(0.37)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.42 <sup></sup><br>(0.36)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.68 <sup></sup><br>(0.39)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.51 <sup></sup><br>(0.37)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Some of the time
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.72 <sup>*</sup><br>(0.34)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.84 <sup>*</sup><br>(0.34)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.63 <sup></sup><br>(0.33)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.04 <sup>**</sup><br>(0.36)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.53 <sup></sup><br>(0.34)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Most of the time
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.43 <sup>***</sup><br>(0.34)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.14 <sup>***</sup><br>(0.34)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.05 <sup>**</sup><br>(0.33)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.46 <sup>***</sup><br>(0.36)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.55 <sup></sup><br>(0.34)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: Some college
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.28 <sup></sup><br>(0.22)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.09 <sup></sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.30 <sup></sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.09 <sup></sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.04 <sup></sup><br>(0.21)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: 2-year degree
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.23 <sup></sup><br>(0.27)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.16 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.06 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.12 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.19 <sup></sup><br>(0.26)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: 4-year degree
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.39 <sup></sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.15 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.06 <sup></sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.11 <sup></sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.09 <sup></sup><br>(0.20)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: Post-graduate
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.90 <sup>***</sup><br>(0.24)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.20 <sup></sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.05 <sup></sup><br>(0.22)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.36 <sup></sup><br>(0.22)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.01 <sup></sup><br>(0.21)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
birthyr
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.01 <sup>**</sup><br>(0.00)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.00 <sup></sup><br>(0.00)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.01 <sup></sup><br>(0.00)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.01 <sup>**</sup><br>(0.00)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.00 <sup></sup><br>(0.00)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
House District: Lean<br>Democrat
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.33 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.13 <sup></sup><br>(0.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.45 <sup>*</sup><br>(0.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.12 <sup></sup><br>(0.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.16 <sup></sup><br>(0.18)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
House District: Lean<br>Republican
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.51 <sup>**</sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.39 <sup>*</sup><br>(0.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.55 <sup>**</sup><br>(0.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.25 <sup></sup><br>(0.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.28 <sup></sup><br>(0.18)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
House District: Solid<br>Republican
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.59 <sup>*</sup><br>(0.27)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.23 <sup></sup><br>(0.25)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.68 <sup>**</sup><br>(0.25)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.40 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.51 <sup>*</sup><br>(0.25)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">
Observations
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
904
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
921
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
917
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
916
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
918
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
R<sup>2</sup> Tjur
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.131
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.035
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.058
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.078
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.010
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
AIC
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
1103.009
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
1269.829
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
1236.507
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
1220.599
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
1262.608
</td>
</tr>
<tr>
<td colspan="6" style="font-style:italic; border-top:double black; text-align:right;">
<ul>
<li>p&lt;0.05   ** p&lt;0.01   *** p&lt;0.001
</td>
</tr></li>
</ul>
</table>
<pre class="r"><code>############################

# Full Model (SB)
glm.sb2.cdR &lt;- glm(RCO322_np ~ 
                     perceive_extreme_very.f +
                     polint.f +
                     educ.int.f +
                     birthyr +
                     cd_presvote,
                   family=&quot;binomial&quot;,
                   data = subset(sbMarginsNP2, pid==&quot;Republican&quot;))

# Full Model (LEO)
glm.leo2.cdR &lt;- glm(RCO321_np ~ 
                      perceive_extreme_very.f +
                      polint.f +
                      educ.int.f +
                      birthyr +
                      cd_presvote,
                    family=&quot;binomial&quot;,
                    data = subset(leoMarginsNP2, pid==&quot;Republican&quot;))

# Full Model (SHER)
glm.sher2.cdR &lt;- glm(RCO323_np ~ 
                       perceive_extreme_very.f +
                       polint.f +
                       educ.int.f +
                       birthyr +
                       cd_presvote,
                     family=&quot;binomial&quot;,
                     data = subset(sherMarginsNP2, pid==&quot;Republican&quot;))

# Full Model (DA)
glm.da2.cdR &lt;- glm(RCO324_np ~ 
                     perceive_extreme_very.f +
                     polint.f +
                     educ.int.f +
                     birthyr +
                     cd_presvote,
                   family=&quot;binomial&quot;,
                   data = subset(daMarginsNP2, pid==&quot;Republican&quot;))

# Full Model (HOUSE)
glm.house2.cdR &lt;- glm(RCO327_np ~ 
                        perceive_extreme_very.f +
                        polint.f +
                        educ.int.f +
                        birthyr +
                        cd_presvote,
                      family=&quot;binomial&quot;,
                      data = subset(houseMarginsNP2, pid==&quot;Republican&quot;))


# Logit 
tab_model(glm.sb2.cdR, glm.leo2.cdR, glm.sher2.cdR, glm.da2.cdR, glm.house2.cdR,
          show.reflvl = TRUE)</code></pre>
<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">
 
</th>
<th colspan="3" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
RCO322_np
</th>
<th colspan="3" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
RCO321_np
</th>
<th colspan="3" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
RCO323_np
</th>
<th colspan="3" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
RCO324_np
</th>
<th colspan="3" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
RCO327_np
</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">
Predictors
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Odds Ratios
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
CI
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
p
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Odds Ratios
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
CI
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col7">
p
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col8">
Odds Ratios
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col9">
CI
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  0">
p
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  1">
Odds Ratios
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  2">
CI
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  3">
p
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  4">
Odds Ratios
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  5">
CI
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  6">
p
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
(Intercept)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
196786293.67
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
3.37 – 13034605361176544.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
<strong>0.037</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
24293240.18
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.31 – 2282431212776253.50
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.068
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
5001966286091.32
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
84957.90 – 376098811433929932800.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
<strong>0.001</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
139703.58
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.00 – 8797482516952.55
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.194
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
9928391.60
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.08 – 1592482021353447.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.093
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
birthyr
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.99
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.98 – 1.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
<strong>0.033</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.99
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.98 – 1.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
<strong>0.046</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
0.98
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.98 – 0.99
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
<strong>0.001</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
0.99
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.98 – 1.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.170
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
0.99
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.98 – 1.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.074
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Solid Democrat
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
<em>Reference</em>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
<em>Reference</em>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
<em>Reference</em>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
<em>Reference</em>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
<em>Reference</em>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Lean Democrat
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.38
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.82 – 2.32
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.227
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.78
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.46 – 1.30
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.338
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
1.21
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.72 – 2.03
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.467
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
0.85
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.51 – 1.41
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.528
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.69
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.98 – 2.97
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.061
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Lean Republican
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.93
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.56 – 1.53
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.768
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.58
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.35 – 0.97
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
<strong>0.037</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
0.82
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.50 – 1.35
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.436
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
0.60
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.36 – 0.98
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
<strong>0.043</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.14
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.67 – 1.99
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.626
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Solid Republican
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.83
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.48 – 1.45
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.523
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.61
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.35 – 1.07
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.086
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
0.69
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.39 – 1.20
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.187
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
0.64
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.37 – 1.10
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.110
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.10
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.61 – 2.01
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.757
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
educ.int.f2
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.32
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.87 – 2.01
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.193
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.18
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.77 – 1.80
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.443
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
0.97
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.64 – 1.48
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.898
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
1.15
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.76 – 1.75
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.508
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
0.93
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.61 – 1.43
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.748
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
educ.int.f3
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.97
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.57 – 1.65
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.912
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.30
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.76 – 2.22
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.332
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
1.33
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.78 – 2.27
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.297
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
1.18
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.70 – 2.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.535
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
0.92
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.53 – 1.57
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.749
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
educ.int.f4
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.35
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.88 – 2.08
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.175
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.14
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.74 – 1.75
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.563
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
1.22
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.80 – 1.88
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.357
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
1.16
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.76 – 1.79
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.486
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
0.85
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.55 – 1.32
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.476
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
educ.int.f5
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.00
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.60 – 1.66
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.988
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.68
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.40 – 1.16
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.165
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
1.16
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.70 – 1.93
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.571
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
1.31
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.79 – 2.19
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.297
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
0.34
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.18 – 0.62
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
<strong>0.001</strong>
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
perceive_extreme_very.f1
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.86
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.63 – 1.17
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.335
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.94
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.68 – 1.28
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.681
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
0.87
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.63 – 1.18
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.360
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
0.89
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.65 – 1.20
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.438
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
0.84
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.61 – 1.16
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.299
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
polint.f2
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.77
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.28 – 2.13
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.604
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
3.73
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.12 – 17.07
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.051
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
1.96
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.70 – 6.11
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.217
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
1.53
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.54 – 4.78
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.433
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.14
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.40 – 3.57
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.814
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
polint.f3
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.24
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.49 – 3.21
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.656
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
4.38
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.40 – 19.36
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
<strong>0.023</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
1.88
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.71 – 5.54
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.220
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
1.80
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.69 – 5.32
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.250
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.96
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.74 – 5.77
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.192
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
polint.f4
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.41
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.57 – 3.63
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.458
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
3.93
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.27 – 17.21
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
<strong>0.033</strong>
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
2.02
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col9">
0.78 – 5.91
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  0">
0.165
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  1">
2.23
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  2">
0.86 – 6.49
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  3">
0.113
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  4">
1.18
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  5">
0.46 – 3.46
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  6">
0.740
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">
Observations
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="3">
694
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="3">
692
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="3">
697
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="3">
694
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="3">
696
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
R<sup>2</sup> Tjur
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="3">
0.034
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="3">
0.030
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="3">
0.040
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="3">
0.028
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="3">
0.045
</td>
</tr>
</table>
<pre class="r"><code>tab_model(glm.sb2.cdR, glm.leo2.cdR, glm.sher2.cdR, glm.da2.cdR, glm.house2.cdR,
          transform = NULL,
          show.ci=FALSE,
          p.style = &quot;stars&quot;,
          collapse.se=TRUE,
          show.aic=TRUE,
          show.reflvl = TRUE,
          dv.labels = c(&quot;School Board&quot;,&quot;L.E.O.&quot;,
                        &quot;Sheriff&quot;,&quot;D.A.&quot;,&quot;U.S. House&quot;),
          pred.labels = pLintraCD)</code></pre>
<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">
 
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
School Board
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
L.E.O.
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Sheriff
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
D.A.
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
U.S. House
</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">
Predictors
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
(Intercept)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
19.10 <sup>*</sup><br>(9.15)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
17.01 <sup></sup><br>(9.31)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
29.24 <sup>**</sup><br>(9.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
11.85 <sup></sup><br>(9.13)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
16.11 <sup></sup><br>(9.58)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Out-party is very extreme
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.15 <sup></sup><br>(0.16)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.07 <sup></sup><br>(0.16)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.14 <sup></sup><br>(0.16)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.12 <sup></sup><br>(0.16)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.17 <sup></sup><br>(0.17)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Now and then
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.26 <sup></sup><br>(0.51)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.32 <sup></sup><br>(0.67)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.67 <sup></sup><br>(0.55)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.43 <sup></sup><br>(0.55)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.13 <sup></sup><br>(0.55)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Some of the time
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.21 <sup></sup><br>(0.47)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.48 <sup>*</sup><br>(0.65)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.63 <sup></sup><br>(0.51)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.59 <sup></sup><br>(0.51)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.67 <sup></sup><br>(0.51)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Most of the time
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.35 <sup></sup><br>(0.47)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.37 <sup>*</sup><br>(0.64)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.71 <sup></sup><br>(0.51)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.80 <sup></sup><br>(0.51)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.17 <sup></sup><br>(0.51)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: Some college
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.28 <sup></sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.17 <sup></sup><br>(0.22)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.03 <sup></sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.14 <sup></sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.07 <sup></sup><br>(0.22)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: 2-year degree
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.03 <sup></sup><br>(0.27)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.26 <sup></sup><br>(0.27)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.28 <sup></sup><br>(0.27)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.17 <sup></sup><br>(0.27)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.09 <sup></sup><br>(0.28)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: 4-year degree
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.30 <sup></sup><br>(0.22)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.13 <sup></sup><br>(0.22)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.20 <sup></sup><br>(0.22)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.15 <sup></sup><br>(0.22)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.16 <sup></sup><br>(0.23)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: Post-graduate
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.00 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.38 <sup></sup><br>(0.27)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.15 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.27 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-1.08 <sup>***</sup><br>(0.31)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
birthyr
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.01 <sup>*</sup><br>(0.00)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.01 <sup>*</sup><br>(0.00)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.02 <sup>**</sup><br>(0.00)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.01 <sup></sup><br>(0.00)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.01 <sup></sup><br>(0.00)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
House District: Lean<br>Democrat
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.32 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.25 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.19 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.16 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.53 <sup></sup><br>(0.28)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
House District: Lean<br>Republican
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.08 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.54 <sup>*</sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.20 <sup></sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.52 <sup>*</sup><br>(0.26)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.13 <sup></sup><br>(0.28)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
House District: Solid<br>Republican
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.18 <sup></sup><br>(0.28)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.49 <sup></sup><br>(0.28)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.37 <sup></sup><br>(0.28)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.45 <sup></sup><br>(0.28)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.09 <sup></sup><br>(0.30)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">
Observations
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
694
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
692
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
697
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
694
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
696
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
R<sup>2</sup> Tjur
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.034
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.030
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.040
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.028
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.045
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
AIC
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
962.105
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
940.361
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
963.606
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
966.202
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
900.828
</td>
</tr>
<tr>
<td colspan="6" style="font-style:italic; border-top:double black; text-align:right;">
<ul>
<li>p&lt;0.05   ** p&lt;0.01   *** p&lt;0.001
</td>
</tr></li>
</ul>
</table>
<pre class="r"><code>#########################################

## INTER-PARTY WITH CD AS KEY IV ##

Finter.house.CD.SolidD &lt;- glm(RCO327_np ~ 
                                pid +
                                perceive_extreme_very.f +
                                polint.f +
                                educ.int.f +
                                birthyr,
                              family=&quot;binomial&quot;,
                              data = subset(houseMarginsNP2, cd_presvote==&quot;Solid Democrat&quot;))

Finter.house.CD.LeanD &lt;- glm(RCO327_np ~ 
                               pid +
                               perceive_extreme_very.f +
                               polint.f +
                               educ.int.f +
                               birthyr,
                             family=&quot;binomial&quot;,
                             data = subset(houseMarginsNP2, cd_presvote==&quot;Lean Democrat&quot;))

Finter.house.CD.LeanR &lt;- glm(RCO327_np ~ 
                               pid +
                               perceive_extreme_very.f +
                               polint.f +
                               educ.int.f +
                               birthyr,
                             family=&quot;binomial&quot;,
                             data = subset(houseMarginsNP2, cd_presvote==&quot;Lean Republican&quot;))

Finter.house.CD.SolidR &lt;- glm(RCO327_np ~ 
                                pid +
                                perceive_extreme_very.f +
                                polint.f +
                                educ.int.f +
                                birthyr,
                              family=&quot;binomial&quot;,
                              data = subset(houseMarginsNP2, cd_presvote==&quot;Solid Republican&quot;))


tab_model(Finter.house.CD.SolidD, 
          Finter.house.cd.LeanD, 
          Finter.house.CD.LeanR, 
          Finter.house.CD.SolidR,
          terms=c(&quot;pidRepublican&quot;,
                  &quot;polint.f4&quot;,
                  &quot;educ.int.f5&quot;,
                  &quot;perceive_extreme_very.f1&quot;),
          dv.labels = c(&quot;CD.Solid Democrat&quot;,&quot;CD.Lean Democrat&quot;,
                        &quot;CD.Lean Republican&quot;,&quot;CD.Solid Republican&quot;),
          pred.labels = pl,
          collapse.se=TRUE,
          show.ci=FALSE,
          p.style=&quot;stars&quot;,
          transform=NULL,
          show.aic=TRUE)</code></pre>
<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">
 
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
CD.Solid Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
CD.Lean Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
CD.Lean Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
CD.Solid Republican
</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">
Predictors
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Party ID
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.36 <sup></sup><br>(0.28)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.03 <sup></sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.44 <sup>*</sup><br>(0.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.81 <sup>**</sup><br>(0.30)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Out-party is very extreme
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.05 <sup></sup><br>(0.25)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.16 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.33 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.50 <sup></sup><br>(0.28)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Most of the time
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.38 <sup></sup><br>(0.54)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.44 <sup></sup><br>(0.58)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.72 <sup></sup><br>(0.51)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.31 <sup></sup><br>(0.71)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: Post-graduate
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.51 <sup></sup><br>(0.39)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.33 <sup></sup><br>(0.30)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.67 <sup>*</sup><br>(0.29)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-1.09 <sup>*</sup><br>(0.55)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">
Observations
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
335
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
492
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
552
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
235
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
R<sup>2</sup> Tjur
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.044
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.013
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.046
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.080
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
AIC
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
440.347
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
683.171
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
735.271
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
317.868
</td>
</tr>
<tr>
<td colspan="5" style="font-style:italic; border-top:double black; text-align:right;">
<ul>
<li>p&lt;0.05   ** p&lt;0.01   *** p&lt;0.001
</td>
</tr></li>
</ul>
</table>
<pre class="r"><code>Finter.house.SolidD &lt;- glm(RCO327_np ~ 
                             pid +
                             perceive_extreme_very.f +
                             polint.f +
                             educ.int.f +
                             birthyr,
                           family=&quot;binomial&quot;,
                           data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Solid Democrat&quot;))

Finter.house.LeanD &lt;- glm(RCO327_np ~ 
                            pid +
                            perceive_extreme_very.f +
                            polint.f +
                            educ.int.f +
                            birthyr,
                          family=&quot;binomial&quot;,
                          data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Lean Democrat&quot;))

Finter.house.LeanR &lt;- glm(RCO327_np ~ 
                            pid +
                            perceive_extreme_very.f +
                            polint.f +
                            educ.int.f +
                            birthyr,
                          family=&quot;binomial&quot;,
                          data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Lean Republican&quot;))

Finter.house.SolidR &lt;- glm(RCO327_np ~ 
                             pid +
                             perceive_extreme_very.f +
                             polint.f +
                             educ.int.f +
                             birthyr,
                           family=&quot;binomial&quot;,
                           data = subset(houseMarginsNP2, Pres_Vote.f==&quot;Solid Republican&quot;))

tab_model(Finter.house.SolidD, Finter.house.LeanD, 
          Finter.house.LeanR, Finter.house.SolidR, 
          terms=c(&quot;pidRepublican&quot;,
                  &quot;polint.f4&quot;,
                  &quot;educ.int.f5&quot;,
                  &quot;perceive_extreme_very.f1&quot;),
          dv.labels = c(&quot;Solid Democrat&quot;,&quot;Lean Democrat&quot;,
                        &quot;Lean Republican&quot;,&quot;Solid Republican&quot;),
          pred.labels = pl,
          collapse.se=TRUE,
          show.ci=FALSE,
          p.style=&quot;stars&quot;,
          transform=NULL,
          show.aic=TRUE)</code></pre>
<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">
 
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Republican
</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">
Predictors
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Party ID
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.43 <sup></sup><br>(0.27)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.22 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.60 <sup>**</sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.78 <sup>**</sup><br>(0.25)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Out-party is very extreme
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.20 <sup></sup><br>(0.25)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.16 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.18 <sup></sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.45 <sup></sup><br>(0.24)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Most of the time
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.10 <sup></sup><br>(0.52)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.06 <sup></sup><br>(0.56)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.55 <sup></sup><br>(0.57)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
1.38 <sup>*</sup><br>(0.69)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: Post-graduate
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.50 <sup></sup><br>(0.35)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.35 <sup></sup><br>(0.31)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.88 <sup>*</sup><br>(0.40)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.64 <sup></sup><br>(0.40)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">
Observations
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
337
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
510
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
444
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
330
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
R<sup>2</sup> Tjur
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.030
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.025
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.060
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.083
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
AIC
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
461.097
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
703.667
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
572.597
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
435.151
</td>
</tr>
<tr>
<td colspan="5" style="font-style:italic; border-top:double black; text-align:right;">
<ul>
<li>p&lt;0.05   ** p&lt;0.01   *** p&lt;0.001
</td>
</tr></li>
</ul>
</table>
<pre class="r"><code>tab_model(Finter.house.SolidD,Finter.house.CD.SolidD,
          Finter.house.LeanD, Finter.house.CD.LeanD,
          Finter.house.LeanR, Finter.house.CD.LeanR,
          Finter.house.SolidR, Finter.house.CD.SolidR,
          terms=c(&quot;pidRepublican&quot;,
                  &quot;polint.f4&quot;,
                  &quot;educ.int.f5&quot;,
                  &quot;perceive_extreme_very.f1&quot;),
          dv.labels = c(&quot;Solid Democrat&quot;,&quot;cd&quot;,
                        &quot;Lean Democrat&quot;, &quot;cd&quot;,
                        &quot;Lean Republican&quot;,&quot;cd&quot;,
                        &quot;Solid Republican&quot;,&quot;cd&quot;),
          pred.labels = pl,
          collapse.se=TRUE,
          show.ci=FALSE,
          p.style=&quot;stars&quot;,
          transform=NULL,
          show.aic=TRUE)</code></pre>
<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">
 
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
cd
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Democrat
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
cd
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Lean Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
cd
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
Solid Republican
</th>
<th colspan="1" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">
cd
</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">
Predictors
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col7">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col8">
Log-Odds
</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  col9">
Log-Odds
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Party ID
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.43 <sup></sup><br>(0.27)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.36 <sup></sup><br>(0.28)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.22 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.03 <sup></sup><br>(0.20)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.60 <sup>**</sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
-0.44 <sup>*</sup><br>(0.18)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
-0.78 <sup>**</sup><br>(0.25)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
-0.81 <sup>**</sup><br>(0.30)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Out-party is very extreme
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.20 <sup></sup><br>(0.25)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.05 <sup></sup><br>(0.25)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.16 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.16 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.18 <sup></sup><br>(0.21)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
-0.33 <sup></sup><br>(0.19)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
0.45 <sup></sup><br>(0.24)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
0.50 <sup></sup><br>(0.28)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
News: Most of the time
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.10 <sup></sup><br>(0.52)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.38 <sup></sup><br>(0.54)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.06 <sup></sup><br>(0.56)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.44 <sup></sup><br>(0.58)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.55 <sup></sup><br>(0.57)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
0.72 <sup></sup><br>(0.51)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
1.38 <sup>*</sup><br>(0.69)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
1.31 <sup></sup><br>(0.71)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">
Education: Post-graduate
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.50 <sup></sup><br>(0.35)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
0.51 <sup></sup><br>(0.39)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.35 <sup></sup><br>(0.31)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.33 <sup></sup><br>(0.30)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">
-0.88 <sup>*</sup><br>(0.40)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col7">
-0.67 <sup>*</sup><br>(0.29)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  col8">
-0.64 <sup></sup><br>(0.40)
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; modelcolumn8 col9">
-1.09 <sup>*</sup><br>(0.55)
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">
Observations
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
337
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
335
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
510
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
492
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
444
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
552
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
330
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="1">
235
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
R<sup>2</sup> Tjur
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.030
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.044
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.025
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.013
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.060
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.046
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.083
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
0.080
</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">
AIC
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
461.097
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
440.347
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
703.667
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
683.171
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
572.597
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
735.271
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
435.151
</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="1">
317.868
</td>
</tr>
<tr>
<td colspan="9" style="font-style:italic; border-top:double black; text-align:right;">
<ul>
<li>p&lt;0.05   ** p&lt;0.01   *** p&lt;0.001
</td>
</tr></li>
</ul>
</table>




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